It's great to be here, and it's quite interesting for me to be here. So if none of you have seen me before, that's because I'm not really a quantum computing theorist. I primarily work on classical algorithms for simulating quantum problems. But I think we all agree that if we're to develop improved quantum algorithms or understand the frontier between quantum and the quantum classical crossover, then we need to understand what is the best we can do with current classical methods. And so that's really what I'm going to try and tell you about today. I'm going to tell you about what the frontiers are when we're trying to simulate quantum problems on the classical computer and where some opportune errors are for penetrating that frontier with near-term quantum hardware. OK. But first, let me just say a few things about chemistry and probably one of the very few chemists in the audience, and most of you haven't looked at chemistry since high school. So in the modern day, what most chemists are interested in is in understanding complex systems of atoms and molecules. And as an example of a complex system, we might take a biological problem such as an enzyme. And of course, enzymes are really central to life. They participate in many processes, such as the reactions which power the planet, synthesis, or reactions which feed the planet, nitrogen fixation. And what the enzymes are doing in these complex chemical reactions is they're helping to shuttle electrons back and forth between the reagents and the products. And these are quantum mechanical events because electrons being very like particles are quantum particles. So what experimental chemists or theoretical chemists like myself are trying to understand is how the enzyme is able to coordinate these different types of quantum events and, in my case, try and understand that through simulation. As another example of a complex system that we're interested in chemistry on the material science, we might take materials with exotic properties. And we all know that you can make materials with different kinds of interesting electronic properties. And one of the perhaps most interesting electronic properties is that most exotic ones is that of high-temperature superconductivity. And actually, this was a phenomenon which was discovered at IBM. This is partly why I'm going to talk about it here. And even though this is something which has been around for now, I think, more than 30 years, maybe it's 31 years this year, we still don't really know why the particular choice of atoms and the range of atoms that you have in high-temperature superconducting materials gives rise to this phenomenon. So this is something that is again a question of modern day chemistry and modern day material science. Okay, so in principle we all know that the answers to these questions just comes by simulating quantum mechanics. Now, this viewpoint was already espoused by Dirac many, many years ago when he recognized that the fundamental laws for most of physics and indeed the whole of chemistry are already known. But he also recognized that solving the quantum mechanical equations, namely solving the Schrodinger equation, is in general pretty difficult for any problem of interest. And indeed there are many pessimistic statements you can find in the literature about the difficulty of solving the Schrodinger equation. I decided to choose the most pessimistic statement I could find, which was made not so long ago by two famous physicists, David Pine, Bob Loughlin, who argued that the Schrodinger equation probably will never be solved when you have more than about ten interacting quantum particles. Okay, so this presents a little bit of a quandary because on the one hand we say we have the fundamental theory of nature. On the other hand, we say we can extract no useful information from it. So the question is what do you then do? Well, of course, as you all know, Feynman said that the solution is just to try and solve these equations on the quantum computer and that's a very natural thing to do and I guess that's part of the reason why we're here today. But the fact remains that at least for any problem of real modern day chemical interest, we don't yet have quantum computers that are powerful enough in terms of the number of qubits available or the coherence time in which they can run simulations. So my talk is really about what we've been doing in the meantime and that sort of brings me to the main part of my talk. How it is that we are able to simulate quantum chemistry problems on the types of computers we have today, which are basically classical computers. And I'll outline the different strategies that we use to try and bypass the complexity in the Schrodinger equation and show a few case studies of real life problems. And this will take up about two thirds of my talk. And then in the last of my talk, I'll try and give a personal perspective on where quantum computing comes in. And this is sort of an opinion, so you should take it with a grain of salt, but I'll try and say where I see some useful developments occurring. Okay, so let's first define the quantum chemistry problem. So essentially, quantum chemistry is just the quantum mechanics of a series of electrons and nuclei. And the nuclei are very, very heavy.  So you can write down a Hamiltonian and it contains a part for the quantum electrons and the nuclei just exerts an external classical potential on the system. And so the primary goal, the simplest task in quantum chemistry, is just to find the low lying eigenstates of this Hamiltonian. For example, the ground state or a few excited states. Okay, so that sort of defines the problem. Now if we write it down in this form, however, it's written down as a sort of continuous eigenvalue problem. And it's not very suitable for computation. So the first thing that people do when doing quantum chemistry is they discretize a problem by introducing a single pascal basis. And in chemistry, that single pascal basis is known, they're known as orbitals. So we introduce orbitals in the problem. And once you've discretized it, then the Hamiltonian assumes it's familiar second quantized form containing up-to-quartic interactions. Okay, so from a computational perspective, the size of the problem is determined by the number of orbitals that you have to simulate. And as it turns out, the complexity of eigenstates of this Hamiltonian by classical methods differs quite widely depending on the form of the coefficients in the Hamiltonian. And in some cases you can simulate many, many orbitals, in other cases you can only simulate a few. But roughly speaking, the types of problem sizes that people are interested in today in chemistry are problems involving on the lower end 40 orbitals. This is in the cases where you have very, very difficult Hamiltonians to simulate all the way up to, say, 10,000 orbitals or even more. Now, if you look at these types of problem sizes, they clearly involve much more than 10 particles. They involve enormous Hilbert spaces. If you think about 10,000 orbitals, that's four to the 10,000, the size of the Hilbert space. And so you can ask, how is it even possible that we can do these calculations on a classical computer? And the answer is quite simple. It's basically because the types of quantum states that you typically see in chemical systems, the natural quantum states, are in fact quite boring. So there's an enormous Hilbert space of possibilities that you can engineer a quantum system to travel into, but the types of states that we see occurring naturally, like the state of this lecture in here or the state of this pointer, are actually quite simple. And so what you really have to do to be able to do a classical simulation is you have to identify what characterizes the simple structure of the states you see in nature. And that's essentially the intellectual content of quantum chemistry, identifying the structure and then using it to perform classical simulations. So roughly speaking, the structure of physical quantum states is imbued on them by the Hamiltonian itself. And the Hamiltonian, as we wrote it down, has two pieces. It's got a first piece, the sort of kinetic energy piece, and a second piece, which is the interaction piece. And roughly speaking, these two pieces of the Hamiltonian create two main kinds of structures that you see in the physical OMG states. So you can imagine a scenario where the first term in the Hamiltonian is the dominant term. The kinetic energy dominates. And in that case, the basic physics of the electrons is that they just spread out. They mainly want to delocalize to minimize the kinetic energy if you're interested in the low energy eigenstates. And in this setting, the corresponding eigenstates are very well described qualitatively by traditional mean field techniques, and then you can improve on the mean field answers essentially by carrying out perturbation theory. So that's the basic classical approach in this regime of physical space. There's the other regime where the interactions dominate. And you might think that sounds very complicated. It's strongly interacting physics. But actually, it's not really. So if interactions are all that occurs in the system and they're very, very strong, then what happens to your particles, because they all want to avoid each other as much as possible, is they'll just crystallize out and localize. So the main sort of qualitative physics in the strongly interacting regime is that of localized quantum objects. And in this case, we can also write down structures to represent the eigenstates based on this local character. And these are so-called local approximations or low entanglement approximations. So what I'm going to do the next 10, 15 minutes is to walk you through the simulation methods with both these two techniques and they'll show you some real-world chemical examples where these techniques are applied. So first, let's start with mean field theory and perturbative expansions. So the kind of chemistry and material systems where this type of theory is appropriate is the chemistry of elements which have so-called S and P valence shells. And if that doesn't mean anything to you, it's basically the chemistry of carbon, so organic chemistry. And it's also the chemistry of simple semiconductors, you know, silicon, gallium arsenide, and so on and so forth. And in these systems, it's generally a good simulation strategy to take your Hamiltonian, break it down into mean field part, which is very, very simple to solve, and then have a leftover perturbative part. And then you can obtain the eigenstates and the energies essentially by Taylor series in the perturbation theory. So you just do a power series in V. And as Feynman showed very long time ago, this is all done very nicely in a diagrammatic language, and you just sum over all the so-called linked diagrams. Now the key thing is, when you're doing perturbation theory, if you stop at any finite order, you have a polynomial cost algorithm for your problem. But if you increase the order of perturbation theory, hopefully, for example, to get higher accuracy, then the polynomial order itself goes up. So the key to the success in this regime of physical space is to be able to sort of beat this race between the perturbation theory converging quickly enough and having enough classical computing power to go to the high enough order in the perturbation series. Okay, now there's a very simple mind approach and you might think it's sort of crazy that just doing a Taylor series and adding in the effects of the interactions to high order should work. But it's in fact the approach that is the basis for the most accurate theories of nature that we know. So if we go to a slightly different domain, QED, then we're all familiar with the idea that in quantum electrodynamics, you can measure something. For example, you can get a fine structure constant, say from the plateaus of the quantum Hall effect. But you can also calculate or infer from calculations the same quantity by summing up diagrams in perturbation theory to very high order, in this case, up to tenth order, and you can get many, many, many decimal places of agreement. This is one of the triumphs of modern theoretical physics. And essentially the approach to simulate chemistry and materials by doing mean field theory and adding up diagrams perturbation theory in this way is really just the same philosophy. And there's a very systematic way to include the diagrams and that so-called couple cluster theory, which we've heard in some contexts already in the quantum computing area. Okay, so let's just see this now at play in a real world example. So I'm going to show you an example of some work we did some years ago doing lattice energies of crystals. So the lattice energy of a crystal is the amount of energy that holds a crystal together. It determines the stability of the crystal. And we're interested in this problem, or this problem is interesting, because if you take typical chemical compounds, which are molecules, when you just write down the chemical formula, when they turn into a solid, when they crystallize, they can crystallize into many different similar forms. They're not the same crystal structure, they're only slightly different, and so we call these different crystal structures where the molecules pack differently polymorphs. But even though the packing is only very slightly different, in many cases the polymorphs have very different properties. And so I can show you some examples you might be familiar with. You know, chocolate is a material where most of us are familiar with. And there are several polymorphs of chocolate, and the chocolate you buy in the store is in this polymorph, which looks nice. The tempering process in chocolate produces a particular packing of the chocolate molecules into this structure. But you all know that you leave chocolate out sometimes under the wrong conditions. It will start looking like this. And this doesn't look so nice. It looks like moulds growing on it, but it's not actually mould. It's just that the molecules have rearranged their packing into different, the lipid chains have moved a little bit, they've rearranged their packing into slightly different crystal structure. Okay, so of course this is kind of a facetious example, but there are also important kind of commercial implications for polymorphism in the pharmaceutical industry. Because in the pharmaceutical industry, you make drugs, these are small molecules, and then you crystallize them, you turn them to solids so people can take them. And these drugs can crystallize, again, into different polymorphs, and the polymorphs can have different bioavailability. And there are famous examples of cases where during the testing and development phase of a drug, in particular this is a famous example for example, people always use the freshly crystallized compound, which had crystallized into one form, and it worked very well, but in the case of Retinovir, when it was finally released, and sat on shelves and so on and so forth, that crystal structure slowly converted itself to a different form, a different polymorph, which was no longer bioavailable. And you can imagine that caused a lot of problems, you know, it had to be recalled, there were all sorts of lawsuits and so on and so forth. So determining the correct and stable crystal structure, most stable crystal structure is an important thing in this field. Okay, so the main challenge is that because these crystal structures only differ by very small packings, they have very, very similar energies. So it's a problem of obtaining very high accuracies. And so we need to compute the lattice energies to very high, to many, many decimal places, to very high precision, which on the chemical scale is about a kilojoule per mole. But this is something that, you know, we can do these days for simple molecular crystals. So I'll show you an example that we did some years ago on a very simple molecular crystal, crystalline benzene. In this case, we can carry out the mean field theory and then add on diagrams, carry out the perturbative expansion to very high order. And so this is what it looks like. So you calculate the mean field energy, then you add on some diagrams and add on some more diagrams and then add on some more diagrams. And you can see the perturbative correction starting to converge down to very small numbers. And you add this all up just like you did with calculating fine structure constant in QED. And you obtain some energy. So this is a lattice energy for the crystal, for molecular crystal. And just like in QED, you can compare the theoretical number with the experimentally measured number. And there's a called widely quoted experimental number for the system. And it's this here, 51. Okay. Now, if you look at these two numbers, you know, they don't actually agree very well, right? So this is 56 to plus minus 1. This is 51 plus and minus 1. They're kind of similar, but they don't quite agree. So there has to be a problem. And so in one case, so you can ask where's the problem, right? So in one case, you know, we're solving quantum mechanics, doing theory very, very accurately, doing perturbation theory until it converges. No, it sounds like there shouldn't be any problem with that. So one would infer that the problems were the experiment. Okay, so the experiment, it's not the usual way that one thinks, but for theorists there's a natural way to think. And indeed that's the case and that's what happened in this system. So after we calculated the results, it was clear that you go back and reexamine the experimental data, and it would seem that the experiments, they had sort of misinterpreted their results. They had missed out some thermal corrections. And after they had put those thermal corrections, then that puts the experiment back into agreement with theory. Very similar to the case in QD, we can obtain not 10 digits of accuracy, but maybe three or four significant figures of agreement. So the point of this story is just to show the good agreement that can be achieved between just simulating the Schroding equation and the real world, at least in this case of very simple perturbative quantum states. Okay, so let me now move on to the second class of quantum states, which are the ones where the repartions dominate and the electrons localize. Now the kinds of chemistry or materials where this is important is really for the elements right in the middle of the periodic table. So you might remember the periodic table has got and then there's a big block of elements in the middle which are known as the transition metals. And in the transition metal atoms, the electrons live in these orbitals known as the orbitals, where they're very sluggish, they just don't have a lot of kinetic energy, so they feel the effect of the Coulomb interaction very strongly. They can't whiz past each other, they just sit and interact with each other all day long. Okay, and so these strong interactions give rise to very rich behavior, and it's certainly no coincidence that all of catalysis essentially relies on these transition metals and the strongly interacting physics. And by the same token, all the really exotic material behavior also involves materials with transition metals inside them. Now the quality of physics, as I've mentioned, is essentially physics of localized particles. Okay, so under strong repulsion, the electrons localize, and if they're completely localized, then they just get frozen out, and all they're left with is then their spin degree of freedom, which can of course flop back and forth. So in the extremely strongly interacting limit, then the physics really just becomes a physics of spins, and that's of course a physics of magnetism. So the strongly interacting limit is very close related to understanding magnetism. Now, if the electrons are not completely localized, so their positions are not completely frozen out, then you can have competition between many different effects. So they might be on the verge of localizing and delocalizing, and all this is going on with some background of magnetism, and this is why you can expect very rich and complicated behavior in these kinds of physical quorum states. But from a computational perspective, it's much more natural in these settings to start somehow from a localized description of the quorum state rather than a delocalized description of the quorum state, and that's the strategy that we take in classical simulations. We try and construct so-called local parameterizations. Okay, so what's the local parameterization or local approximation? I don't have time to go into all the details, but very roughly speaking, we want to characterize the quorum state in some kind of local variable. It might be local coefficients in some local Hilbert space. It might be local expectation values. And the advantage of doing that is instead of having to deal with, say, the wave function, the global space, in which case you have some exponential dependence on the system size, if you're only dealing with wave function in little bits of space, then you basically lose the exponential scaling and you get some kind of polynomial cost parameterization of your problem. Now, probably the most well-known example of this, probably in this community, is the so-called tensor network formalism. And very roughly speaking, what you do here is you write that wave functions in your local Hilbert spaces, and they're not quite wave functions. They have some additional mathematical indices which make them tensors, and then by gluing together these indices you can recover the global state. But this is not the only way to construct a local approximation. Another way is to characterize your system in terms of local observables, not local wave functions, so local expectation values. And this is the basis of so-called quorum embeddings where you just use local observables and then glue together them in some way to get a global description. Okay. But let's now go on to a case study and see how some of this works in practice. I'll think about whether to talk about biology or materials, but, you know, as I said, high-temperature superconductivity was discovered at IBM, so I think it's a good thing to talk about here. Okay, so high-temperature superconductors, you know, the main class of these materials are the so-called cuprates. There's another class, but the cuprates are perhaps the most famous class. And there are many different cuprates. They contain the atoms in different arrangements and so on and so forth. But if you look at the phase diagram what that tells you is that the superconductivity has some sort of generic quality or it has some universal quality and it can be described in terms of a simpler model. And so the most famous model and perhaps the most fundamental model which to understand high-temperature superconductivity is a so-called 2D Hubbard model, what people here would call the 2D Fermi Hubbard model. And it's described by Hamiltonian, which again has these two pieces, the kinetic NG piece has been greatly simplified and the interaction piece has been greatly simplified. So one of the first tasks, as a theorist, trying to understand high-temperature superconductivity is to ask, can we compute all the properties of the 2D Hubbard model in order to obtain the accurate phase diagram? Now, many people have worked on the 2D Hubbard model over the years. That's a slight understatement. And historically it's been a bit of a graveyard for classical numerical methods as well as the inventors of those methods. But recently a lot of progress has been made. And to illustrate what I mean by this progress recently in a collaboration between many different numerical theorists, including myself, we compared different state-of-the-art classical approximations for the energy of the Hubbard model at a particular point in the phase diagram, which is not important at which point. And all these different methods, many of which use these type of local approximations that I introduced a couple of slides ago, give almost exactly the same energy. So that's telling us that we can now compute the energy to many, many significant figures, say three or even four significant figures. And this is to be compared with what you could do about 10 years ago where you could only get about one significant figure or so. So what this means is that there's been two orders of magnitude improvement in accuracy in the last decade, and that's what's finally allowing us to obtain definitive solutions of this basic model of high-temperature superconductivity. And to just show you one of the, I think, one of the results that we're most proud of, very recently it's been possible to see exactly how the high-temperature superconductivity develops inside the Hubbard model itself. So you can try to study this by asking what does the material, this is a theoretical material, the Hubbard material, look like before it's superconducting, just before it's superconducting. And what you find then is that the charges of the materials spontaneously crystallize out into these one-dimensional structures called stripes. And then even more intriguingly, you find that if you allow these stripes to wiggle, you squeeze them together, you allow them to fluctuate, then you see the pairing develop between the electrons themselves. So this is a very evocative picture of how high-temperature superconductivity emerges, and it's a sort of numerical confirmation of a long-standing conjectural, long-standing hypothesis that arises from fluctuating stripes. But in any case, this is just an illustration of the kinds of things that we can do with classical methods these days within the strong coupling regime of quantum problems. So let me now move on to the next part of my talk, which is about quantum algorithms. So so far I've told you about what we can do with classical methods, and the key point is that they're often very effective, in some sense they're unreasonably effective, simply because the states we're studying are not so complicated. So the important question to ask is, where do we then look for quantum advantages in real quantum simulations, or simulations of real quantum materials? And what's the practical boundary between quantum and classical algorithms? A related question one might also be interested in is, given all the domain knowledge we've developed in the world of classical simulations, can we use it to improve quantum algorithms? And that's something I've recently become interested in, but I won't talk about this here because the work that I've done in this area is going to be described by Ryan in the next talk. Okay, so let me focus on these questions. Okay, so one way to ask, you know, what's the practical boundary between quantum and classical algorithms is to say, if we just froze all progress in classical computing today and the development of classical algorithms, what exactly can we do? Can we put some numbers on that? Okay, so first let's look at weak coupling problems, which are mainly the kinds of problems you study in quantum chemistry. And if we're interested in getting the ground state energy of a typical weak coupling molecule to an accuracy of about 1 kT per bond, and I choose this accuracy to see energies to this accuracy, then you know the reaction rates to an order of magnitude. Then with current classical techniques, you can treat really quite large systems, you know, thousands of orbitals. And that tells you that this isn't quite the best, this isn't quite the right frontier to see immediate crossover with quantum algorithms. However, if I change the question a little bit, if you remember these energies or these calculations are being done in some sense with an underlying perturbative framework, and if you want to go get higher accuracy in the perturbation theory, you have to go to higher order, which is more expensive. So if I change the question a little bit and say, what if I want the ground state energy to a higher accuracy, let's say 0.1 kT per bond. And this is still a reasonable accuracy, everyone would like to have energies to 0.1 kT per bond.  Then with current classical techniques, you can treat much smaller systems. You'll need maybe a hundred orbitals or so. And that's now not looking so far away from where you could get a near-term crossover. And then finally, if we move from looking just at the lowest eigenstate of the Hamiltonian just to say the second or third eigenstate, it's empirically observed that in most cases these perturbative methods don't work as well. And so you have to go to higher order. So if we look at the excitations of the system and even ask just for a more discrease, which is 0.1 EV if people are thinking chemical units, then the classical frontier is smaller still. So that tells you that if we're trying to find the best place to see this type of quantum classical crossover, we should ask the right question. And in particular, if we insist on high accuracy and look at beyond ground state simulations, then even in small molecules, it may be possible to beat current classical implementations. Okay, now moving into the strong coupling regime, which is typically a more interesting intense method of physics, we can think about, say, moving around in the phase diagram. And in certain points in the phase diagram, it's much like the previous slide, classical methods do really, really well. So in the case of the Hubbard model, at near-harf filling, you can obtain extremely high accuracy. There's really no point in trying to do that with a quantum computer. But if you move away from the Hubbard model, for example, in the regime where these stripes are seen to appear, then the accuracy you can obtain is much less. It's only about 1%. And that's enough to kind of answer some physical questions if you're very, very careful. But it's not enough always to have an unambiguous answer. So in the case of looking at the origin of superconductivity and stripes in the 2D Hubbard model, we had to use many, many different classical techniques and cross-check them to make sure they gave a consistent answer because a single method by itself was not sufficiently reliable. And it would be great if a quantum computer could, say, obtain more than a better than 1% accuracy because then we could have a one-shot calculation to understand the physics. And then finally, if we move away from the ground state, for example, to low but finite temperatures, currently there are no good classical methods. So again, by tuning the problem in the right way, we may be able to beat classical techniques. I just want to finish by saying that a point I'd like to make, however, is that although a lot of quantum simulation that I see today seems to be focused on the ground state, it's not really clear that that's the best thing to focus on. And in particular, I think quantum dynamics is perhaps a bare target for seeing near-term crossover between quantum and classical methods. And that's because we know it's generically hard on a classical computer, but quantum dynamics as well, a quantum computer, is doing all day long. So from a classical perspective, we understand the complexity of doing quantum dynamics by saying exact quantum dynamics, by saying generically the entanglement in the quantum system, if you just allow it to evolve, grows linearly with time. And so if you construct some kind of classical approximation to the dynamics, such as a tensor network simulation, then you'll find that you start to need an exponentially growing set of parameters to capture the quantum state as a function of time. A point I want to make is that when we're dealing with quantum dynamics with these reasonable Hamiltonians, these physical Hamiltonians, we don't think of the classical algorithm as really having an exponential dependence on the number of qubits. It only has an exponential dependence on the amount of time you're simulating. So it's very different from the kind of Google problem where you're doing these randomized circuits and the classical complex itself depends on the number of qubits. In most physical simulations, we really care more about the time you can simulate, than the number of qubits. Okay. So if we really take quantum dynamics seriously as the testing ground or the first place to see a crossover between quantum classical techniques, it's important to ask if there are any classical loopholes in the complexity. And the question which I think needs to be answered is for the kind of questions that we ask, which is what is the value of a typical average observable? Do we really need to have a faithful representation of all the entanglement? And this is something which we don't know the answer to today, but there are some hints that in some cases you don't actually need to model all the entanglement system to answer the typical questions you want to know. So to give you an analogy, it's sometimes forgotten that even if you do classical dynamics, so solve Newton's equations, the cost of solving Newton's equations faithfully increases exponentially with time because of chaos. So because of chaotic trajectories, you need to get, solve the trajectories to more and more precision to get the trajectories correct. But on the other hand, this type of, the fact that we can't simulate classical dynamics even precisely at very long times doesn't actually matter because the quantities we're interested in are always averaged either over some time or over a large number of atoms. So the consequence in quantum systems is that if you take quantum dynamics for a large set of particles over long enough time, the presence of chaos should lead to an emergent hydrodynamics. And the hydrodynamics may be something you can simulate without too much entanglement. And there's certainly evidence for this in some numerics. For example, if we try and calculate the diffusion coefficients, one finds that you can get it right in classical simulations as a function of entanglement, even in simulations of very low entanglement. So these are questions which still remain to be characterized by I think they're important to think about. But if we just say, let's imagine that we make no progress in classical algorithms and ask what is the current classical limit to simulating quantum systems in real time, we can make some estimates. So let's take a physical Hamiltonian, let's say a nearest neighbor Hamiltonian, and let's think about carrying out the best possible classical simulation. So let's take for example a tensor network algorithm, you build it forwards in time to make a very complicated structure with a lot of depth. And eventually you run out of steam and you can compare that to the same simulation on a quantum processor and run it for a long period of time and eventually it runs out of steam and can just ask which will lose steam first. And we can estimate the maximum depth you can simulate in a classical tensor network, really from the largest tensor network calculations that have been carried out in ground states. And this goes right to an effective depth of 256, this is just something that's characterizing the depth of this simulation. And if we think about what this translates into real-time units, it's roughly a classically accessible time of four units of Hamiltonian time. So this is all back of the envelope but this tells you about roughly how far we can simulate out in time, currently on a classical computer. And if one can go further than this with a near-term quantum device, then essentially you'll have beaten all the classical algorithms for dynamics. Okay, so that really brings me to the end and to my conclusions. So the take-home messages are that classical algorithms are quite capable for many different problems but if you tune your problems correctly you should be able to see this quantum computing crossover and it's clearly time to push on that frontier. Thank you. Thanks Garner for the talk. We have time for some questions. So thank you very much, that was really interesting. I like the idea that dynamics are potentially the best application for quantum computers simulating quantum systems. What questions about dynamics are really urgent and interesting? I mean, if I want a $10 million dynamics question, what would it be? Yeah, so I think it depends a lot in the discipline you're working in but I think in the... For a long time it's been hard to ask the question what do we want to study with dynamics because it's also a question of what experiments can really characterize in dynamics. But nowadays we have free electron lasers which basically can pump materials into out-of-equilibrium states and so I think one of the very interesting things that people try to understand theoretically is if you pump, you know, you excite the entire material, produces a number of excitations, can you change its phase, can you induce new kinds of, for example, transient superconductivity and so on and so forth. Is there a dynamical phase transition? So I think these are questions we can all agree on are interesting. But it's true that for a long time it wasn't quite clear what one should study in a dynamical area. Thank you for the talk. I know that what you call quantum chemistry has undergone its own quite rapid development as well as quantum computing has and you talked about that order of magnitude improvement or the past decade, that one example. And one question I've had for a while, actually, was is that this development of classical simulation of quantum chemistry, has it come about from classical algorithms being developed that better tackle that quantum approximation or has it taken sort of a different sort of direction from what we're sort of doing? Is it just all about parallelizability and that kind of thing? Yeah, you know, some comes from both but I think that the... and I think the main things have come about from having a better understanding of the problem. So in other words, if we have a better understanding of the types of states that are to the typical ground states the better types of wave function ansatz and approximations, that's probably what's led to the biggest improvement. But there's of course been a lot of technical improvement as well ranging from, you know, parallelization to just better ways of computing numerical integrals, you know, lots of things like that as well. So Garnet, can you hear me? Yes, another chemist. You focused largely on the minimum energy structure but of course we're interested in mapping out potential energy surfaces. And so there are a lot of difficulties with transition states and very mixed states that don't have Born-Oppenheimer states. Could you say something about what you think the current limits of those are? On a classical? On a classical, where you might get some bang for your buck on a quantum computer. Yeah, so, you know, I focused on calculations, mainly because that's what my research is about, first on calculations where the nuclei are stuck, you know, they're basically fixed. But there's of course a whole layer, many layers of the field where you're concerned about how the nuclei themselves move around and move into different structures and sample different pieces of spaces as well. So I think there are two parts to that problem. So one is you need to be able to calculate the right energy for a given set of nuclear configurations and you need to be able to calculate that very quickly. So that's one problem. And the other is you need to be able to solve the classical sampling problem. In other words, you need the nuclei to explore the phase space, the classical phase space as quickly as possible. So there have been many developments in both of these, both of these areas as you know. I mean, I think the most difficult problem in the sampling problem is the fact that although the atoms move on the time scale, that is say, picoseconds, there are many events, for example, protein formings which take place really on very long time scale, like a second. And so there's 10 to the 12 type of atomic motions you have to consider before you see one event of the protein folding. And how you bridge that time scale is a big issue and there have been advances by developing specialized classical hardware for that problem. How a quantum computer might deal with that, I don't know. I mean, how it can solve this time scale problem, I don't know. Okay. If there are no further questions, let's thank Arne again.


Welcome folks, this is Tom O'Brien of TFNN. We have five days a week, we go seven hours a day, we go 24 hours a day on the internet at tfn.com. Always remember folks, what have you think about, you bring about whatever. You focus on growth, hope everyone's having a great day, safe day, it's a TGIF folks, and for us, market participants, it's a three-day weekend. Don't make assumptions, ask for what you want. Find the courage to ask for what you want. Others have the right to tell you yes or no, but you always have the right to ask. Likewise, everyone has the right to ask you for what they want, and you have the right to say yes or no. Market-wise, let's take a look at it out here. We have the Dow Industries up 108, Nasdaq down 80, S&Ps down 14 and a half, gold. Gold contract up 90 cents, traded 18.52 an ounce.  Our phone number's<PHONE_NUMBER>. Give us a call, folks. One note's going on in your world, and the world of the S&Ps, let's take a look at them. What do you have?  You're gonna have an expansion of volume here. So we're gonna have, regardless of what happens, you're gonna break the swing with volume. The swing we're talking about is 40501, you hit 40405, you get 59 million shares, you only need 70 million, yeah. And you're gonna have a lot more than that. So that means you're gonna be right back down to this level. And see, that's where we were on top side from. So what you have here now, the S&Ps in the lower range. Anything under 410 is the lower range. And what that sets up, folks, that sets up the lowest swing that does have volume also at 374. And the X100, we take a look at the three Qs. What do we have with the three Qs out here? Three Qs right now. You're gonna have volume there too. So the swing on the three Qs has 50 million. Yeah, you're already at 45, man, yeah. So the three Qs that now they haven't got inside the lower range yet. The lower range inside the Qs, realistically, I'd be happy with, not happy, meaning, you know, see, there's a couple of spikes. Like, and I wouldn't use the 296 for the lower range. You're gonna lose the lower one, which is 293. And right now you're at 301. I suspect it's gonna try to get down then. I will see whether it's gonna get into that lower range. Gold, gold contract. We take a look at the gold contract. Gold contract, 156,000 contracts out here today. It's rejected lower price. You're at 1852 right now. And we'll see if we can get any juice going. You know, bottom line, and it's gonna be all about this good old U.S. dollar. You go over to the dollar, we take a look at the dollar, what you have with the dollar. As they gave it up on price today, you know, we'll see whether it basically comes right back at ya. You know, in order to, the dollar right now looks to me still that you're gonna go to the 106, 107 level. We got to the 104667 today. That's how this baby is shaking out. Oil, we go to the oil market. Oil's giving it up in spades, man. Would you take a look at this oil market out here. You're down high, 65,000 contracts. Oh, that's not a lot. We're rolling, I see what's happening. Okay, interesting. So let me do this, CLA. Is it gonna give me the same contract? Yeah, we're rolling. Okay, we're rolling, but the bottom line is you're not holding price. So this baby's gonna go right back down to the bottom of the range. The bottom of the range, you know, in this particular case is about the 72 to 70 area. We'll see what that's gonna shake out. Some of the higher volume equities, and we will have volume out here today, folks. You have Teslas up $5. You got DraftKings up two and a half. Let's do it this way. I'm actually gonna go into the NDX first. Let's go into the NDX. See what's moving the market. So MGEN is up 2.5%, Tesla 2.5%, Biogen 2.5% and Regeneron 2.4%. Taking away from it. Airbnb down five, fang. What is fang, man? Oh, diamondback energy. Down five, you got Baker Hughes down four and a half, Moderna's down 3.8%.  I picked three trades out there. I was going to shot this pioneer natural, man. Now, the bottom line is that I send these stocks over to them at seven o'clock in the morning, folks, okay? This thing gets smoked, man. I mean, it's like, in three days, it just went down from a price point from 234. It hit 206 today. I was looking for the 205. So I was like, it looks to me like this thing here is gonna go to 200 now. Now, it's dangerous about this. 200 is the swing, but you break 200, you can see. This is barreling down, man. This is on the weekly, you can see the expansion of volume. Your swing point down here has 170 million. You've already done, no, no, couldn't be, what? Oh, no, 9.1, and we've done 13. So this thing's gonna go a lot lower, man. That's how that baby is set up right now. The Dow, Dow Industries inside the Dow. Let's take a look at the strength versus the weakness in here. Point-wise, what we have, United Health putting 70 positive points. Amgen 41, Big Mac 24, Traveler's 20. Take it away from it. Microsoft, minus 29, Home Depot, minus 23. You get Chevron, minus 22. We go take a look at, yeah, there's another one. I picked this one out, too. This is Norfolk Southern. Norfolk Southern, they bought a mine. They got in a big accident. You know what's wild about Norfolk Southern folks is this, is that even, I'd always love to, when this happens, it's like, okay, it has a high volume low that it wants to go to, meaning the 203. But it's like, even when you're up at highs, I mean, this thing was up at highs of 261, got in the accident, it was a trade at 255. This thing is, it's gonna get down to that level. And it was gonna get down to that level prior to the aspect of, look at the volume on this thing, prior to the aspect. As you can see, this high volume high load that's laying out here at 203. That's always intriguing to me, like, okay, how technically does that set up that it wants to get down there, then all of a sudden something happens and then, ba boom, it goes there much faster than you'd realize that it wants to go there. So pretty, pretty wild, there's no doubt about it. Some of the other high volume equities out here that we have today, you got Southeast energies down at 26 bucks, you got Google off a buck and a half. Let's see, what do we got? Devon energy, energy's getting beaten up here. Stay right there folks, come right back.


Ever heard the old phrase, strike when the iron is hot? Well, it's never been hotter than now. All this month at Appalachian Wireless, you can get the Samsung GS6 or the GS6 Edge for just one penny when you send them for service. The Galaxy S6, normally $49.99, now just a penny. The Galaxy S6 Edge, normally $99.99, now just a penny. Strike now while supplies last. Better service, bigger savings, serving you for the last 25 years at Appalachian Wireless. The Kentucky Department of Agriculture's Hunger Task Force held its last of 10 regional meetings at the University of Pikeville to hear from volunteers who fight hunger locally to better come up with solutions to alleviate hunger in Kentucky. We hear that a lot of times infrastructure is a challenge with food pantries. Many of our pantries are run completely by volunteers and they don't have refrigerated space and so if a farmer wants to provide produce or a retailer wants to provide milk they may not be able to accept it because they don't have enough refrigeration space. Another common challenge is transportation. Getting clients who need help to the food pantry can be a challenge. Getting food that's donated by farmers to the food pantry is another challenge as well. Although the Task Force will take information gathered from the meetings to use in creating a strategic plan to address hunger, they have already heard many innovative solutions. We're coming up with some innovative solutions. One is to help buy food that would otherwise rot in the field from Kentucky farmers and get those in the hands of those who need it. We're looking at backpack programs where we fill backpacks up of those kids that we know may have a food insecurity issue so they go home and have food for the weekend and come back to school Monday healthy and ready to learn. Of course another thing we're trying to promote is a culture of gardening. The gardening is more or less lost art but here in eastern Kentucky the gardening is strong and I think it's a solution that we can help incorporate at our churches and also our school systems to help provide food for those who need it. For more information on the Hunger Task Force simply visit the Kentucky Department of Agriculture on the web at www.kyarg.com. Reporting from Pikeville for EKB News, I'm Shawn Allen.


You know, people always ask like, man, how do you get rich? How do you create a great life? How do you have fulfillment in your heart? Well, just give all you got. And right now, there's a lot of you guys that have resources inside of you. The difference between rich people and poor people? Resourcefulness. Rich people are very resourceful. They team up with people. I'm a resource to you guys to hopefully help push you guys every single day towards the direction you want.


All right, the new year is just around the corner, and with the new year comes a clean slate, an opportunity to re-evaluate your life and make goals to create what you really want 2022 to look like. Yes, career, spiritual, and relationship intentions are very important, but I'm going to focus on health resolutions today. After all, when your health is taken care of, everything else is easier to tackle. And I'm not talking crazy over the top, lose 100 pounds in a month, new year resolution, but small health goals that you can actually stick to. In my practice, I've observed that patients that make these kind of micro goals are really the most successful. So today I'm going to show you how to beat that awful statistic that most people ditch their new year's resolutions by February. True statistic. By making easy minor adjustments to your existing routine rather than overhauling everything. So stay tuned because I'll share five simple resolutions that will make a drastic improvement in your health so that you can enjoy a longer, happier, and more energetic life. Today I'm talking about new year's resolutions and which will make the most dramatic impact for the least amount of effort. Sounds good. Well, let's dive right in. Number one, the seems self-explanatory, but resolution number one is move more. If you don't exercise at all, take the challenge to start small with five to ten minutes of exercise per day. Even if that's just walking around the block or dancing to one entire song like I talk about in the energy paradox, it's a great start. Then slowly increase your workout time every week. So if you commit to five to ten minutes per day, increase your movement to seven to twelve minutes per day the next week and so on until you're working up to about thirty minutes a day, even if it's just ten minute exercise bouts three times a day. And here's the deal.  Take the elevator up, but walk the stairs down. Beautiful experiments that I've alluded to from Austria allowed people to either take the cable car up to the top of the mountain and walk down or walk to the top of the mountain and take the cable car down and lo and behold, they found that either way got the same beneficial results. Any time you walk against gravity, up against it or down against it, you'll get a huge amount of benefit. And as most of you know, one of the unique unifying features of all the blue zones is that they all live in hilly communities. And so just go walk. And another great idea to make you move, like I always say, is get a dog, preferably a rescue dog. This past weekend, I took our rescue dog, Okidoki, a miniature poodle out on the runway for the Good Samaritans Animal Shelter in Palm Springs area for men of the desert. And get a rescue dog. You'll benefit them, they'll benefit you. I just actually gave a prescription again to one of my new patients this week to go get a dog. And last week, one of my patients who I had given a prescription to a couple months ago brought in their new rescue dog and it was a great little dog. So get a dog. Now even if you can't have a dog, consider volunteering at these shelters. I can tell you they need volunteers and you'll get working with the dogs out in their runs and you'll benefit, the dog will benefit and who knows, you might take one home. Now if you already have a good exercise routine in place, look for opportunities to get even more movement throughout the day, not just once a day. And I talk a lot about this in the energy paradox and I call it energy snacking, exercise snacking. So take the stairs for a few minutes. I know this sounds trite, park your car way away from where you're going into a store. My wife and I just actually did this two weeks ago at Costco and I was grumbling the whole time but in fact it was a brilliant way to get in, you know, about a five minute walk in the Costco parking lot. One of my favorite tricks is do squats when you're brushing your teeth morning and night. You're not doing anything for a minute or two except brushing. So just do deep knee bends. Do it while you're washing dishes. Do it while you're folding laundry. Any opportunity to sit up and squat and stand up is a great opportunity to get some exercise snacking done. Now they may seem insignificant but there's multiple studies showing that these little one to two minute bursts of exercise can have just as much impact as 10 to 20 minutes of exercise in a continuous fashion. So anytime you get your muscles moving is a good time. Alright second project, improve your metabolic flexibility. Did you know that roughly 98% of obese people have zero metabolic flexibility?  Mock flexibility means normally the mitochondria that live in almost all of our cells, those little organelles that make ATP, the energy currency, they can make ATP by using glucose or amino acids, protein or fat, free fatty acids to generate ATP. But they can't do it simultaneously. Now most of the time mitochondria like to burn glucose to make ATP. But if glucose isn't available then just like a hybrid car, a mitochondria should be able to on a dime switch over to burning free fatty acids as a fuel. Or the brain can burn the equivalent ketones as a fuel. But what is sadly missing in Americans in particular is that the vast majority of overweight and obese people, in fact almost all obese people cannot flip the switch when glucose runs out. Now what does that mean? Well it literally means that our brains for instance, if glucose is no longer available, if you stop eating and 12 hours pass, in the middle of the night your brain which normally should be now running on ketones, instead there are no ketones to use, you can't make them and you can't burn any fuel. And so your neurons literally start to die.  And so it's actually no wonder that we're seeing this epidemic of kidney failure, liver failure, heart failure, brain failure which we call dementia or Parkinson's or Alzheimer's. It's because our bodies no longer can make this switch between fuels that's actually so dependent on good functioning.  Okay, so how do we get our metabolic flexibility back? Well that's what I spend most of my practice with my patients teaching them how to do. And as you know I explain this in great detail in my book The Energy Paradox, but I've created a very slow, gentle program to retrain your mitochondria to become metabolically flexible. And it really only takes about six weeks. So one of the most empowering things that you can do and I want you to start January 1st is shortening the time of day that you eat to about six to eight hours. Now what does that mean? It means that if I asked you on January 1st not to eat breakfast, break fast until noon, the vast majority of us would fall flat on our face because we couldn't burn fats as a fuel and we would get hangry, we'd get headaches, we'd get weak and we'd fail miserably because the vast majority of us can't do that. But almost every one of us, if say we eat breakfast at seven o'clock in the morning, can make it till eight o'clock in the morning and still do okay. So as I talk about in The Energy Paradox and soon in Unlocking the Keto Code, the first week I want you to eat breakfast at eight o'clock in the morning, all week.  Then next week I want you to move your breakfast to nine o'clock that week. And for five days eat breakfast at nine o'clock, take the weekend off. Week three, we're going to eat breakfast, break fast at ten o'clock. We're going to do that for five days, take the weekend off. And so on. As you can see in five weeks, you'll be eating break fast at noon, at lunch. You're going to get there gradually. It's exactly like starting any other exercise program. I got news for you, unless you practice, you're not going to go out and run a 5K or a 10K or a marathon, just running out the door. You have to build up to that. And it's the same way with metabolic flexibility. You have to build up to making your mitochondria flexible again. And this program, slowly but surely, ask your mitochondria and learn how to switch between burning sugar for fuel and burning fat for fuel. Now the great news is, by the end of five to six weeks, this will become second habit and programs that have been done in humans, and certainly in my practice, if we give you the ability to relax on the weekends, anybody can do this for five days in a row. And it makes it a habit that you can live with, literally and figuratively. Okay, so that's getting your metabolic flexibility back. And it's absolutely important for long-term health of every part of you. All right, third project. I want you to really start thinking about limiting or even eliminating red meat. Now most Americans have red meat almost every single day, from burgers to steak to bacon to pork chops. You name it. It's part of our American diet. And don't get me wrong, I grew up in Omaha, Nebraska. There's several bad things that happen, and it has nothing to do with cruelty to animals. We're not going to go down that road today. But I'm talking about cruelty to ourselves, our animal that we are. First of all, as many of you know, have read my books, beef, lamb, and pork all contain a sugar molecule in their blood vessels that's called Nu5GC. Humans, fish, and poultry have a different sugar molecule called Nu5AC. It turns out that those sugar molecules are very, very similar. But we make an antibody to Nu5GC when we eat these animals. And that antibody is very interested in the sugar molecule in us. And there's very good data that the reason there's more heart disease among red meat eaters is that we actually have an autoimmune attack on our blood vessels. The other worrisome factor is that cancer cells use Nu5GC to hide from our immune system. Now, there's a problem. We don't manufacture Nu5GC.  So think about that the next time you're having a juicy steak, or a burger, or keto bacon, you're actually eating a sugar molecule that gives cancer cells in you an advantage to evade your immune system, puts bacon in a whole new light. Now the other thing that's important about animal proteins in general is the development of advanced glycation end products, which are better known as ages, because advanced glycation end products age us. There are two contributing factors to developing ages in us, and we do it 24 hours a day, folks. The amount of sugars that we eat and the amount of protein that we eat. And these sugars and proteins are actually bonded together in the presence of heat, and I got news for you, there's heat in our bodies, to create one of the strongest chemical bonds that's ever been described, which is advanced glycation end products, ages. We actually measure a blood test for how many ages are occurring in you. And that's hemoglobin A1C. And that actually looks at how many of these compounds you're generating for about the two months prior to the test. We have a recent test that's either called fructosamine, or glycated proteins, there's that word glycation, that looks for about two weeks of how many sugars and proteins you're eating. And I can tell you my patients are flabbergasted now that we've been using that test routinely to see in the two weeks prior to their blood tests the amount of glycated proteins present in their bloodstream. And this is going on whether we know it or not. And these things make us stiff. It stiffens our heart muscle. It stiffens our joints. These things occur in our brain, and I don't particularly want a stiff brain. So there's some pretty powerful reasons that we should at least lessen the contribution of animal proteins into our diet. All right, so how do we do that? Swap out red meat portions for grass-fed, grass-finished poultry. We're beginning to find it more readily available, granted it's still hard to find. Or better yet, wild-caught fresh seafood or shellfish. There's actually plenty of available frozen wild shrimp at markets like Trader Joe's at Costco. There's wild frozen salmon pieces at Costco. This is becoming much more easier to find. Now the other important thing is, let's think about portion size. Really, you only need about a four ounce piece of animal protein in 24 hours. That's about the size of a deck of cards. If you want to use the palm of your hand and the thickness of the palm of your hand, that's okay. And guys who are giant football players, your palm doesn't count. But the more we can kind of think about diminishing that portion, the better. Often when my wife and I are at a restaurant, we'll split the fish portion. And there's still plenty for both of us. The other thing to remember is, I want you to think about eating for two people simultaneously. I want you to think about eating for your pleasure, your taste buds, as one for me. And then I want you to think about two for them. So for every bite of animal protein, I want you to take two bites of plant fibers for them. The more important guy is your microbiome.  And that's your microbiome. So I don't want you to give things up, but I want you to make room in your stomach and in your intestines for what really matters. And you'll do yourself a favor in two ways. When I was growing up, we actually used to have Meatless Mondays or Tuesdays, where the whole nation would give up meat. There was a lot of pasta, I can tell you, but it doesn't have to be a pasta dish to replace. But start with one day a week where you just don't eat any animal protein. It's really easy to do. If you can't do that, then one day a week give up beef, lamb, or pork for one day a week and work your way down. Now for me personally, I eat red meat or poultry, actually a few times a quarter. And I have shellfish often on the weekends. But the rest of my week is pressure cooked legumes, lectin-free grains like sorghum and millet, tons of vegetables, and of course everything is smothered in olive oil, including the shellfish, including the poultry. And it's a great way to go. So just challenge yourself at the start of the new year, at least one day a week, just eliminate animal protein and see how you do. Restaurants. Restaurants, as you know, I'm friends with many James Beard award-winning chefs.  And that's where they make their money, that's where they make the name. They actually tell me that they have much more fun doing appetizers and vegetable dishes where they can actually be far more creative. So what I'd like you to do is look at all the greens, look at all the fresh vegetable appetizers, choose those, and build a meal around those. And we call it grazing, and if you follow me to a restaurant, you'll see often that the grazing is most of the meal. And my wife and I may have four vegetable dishes that we share and one fish dish that we split or not even a fish dish. So just think about maximizing your options in a restaurant. They're actually hidden there for you. Okay, next project, monitoring your health. Now most of us have been told we've got to track our spending habits. They're much more likely to be committed to your health goals if you're accountable for tracking those. And one of the best ways to monitor this progress is a fitness health tracker. There are wonderful devices on the market. I like the aura ring and the whoop band. My whoop band's hiding under here. Just got my new next generation whoop and aura rings. They both get better and better. The Apple Watch is good. It's a good fitness tracker. There's lots of other really good fitness trackers. Fitbit just as an example. Polar makes some great fitness trackers. Get one and just start using it. Now, they're not perfect. They're not 100% accurate. Now shall I say designed to over-reward your effort. So don't necessarily believe the number of steps that are real. But they're great for looking at trends. And they're great for a lot of these devices now give you feedback. They applaud you and give you great job. And they're very useful and they're fun to play with. The other thing that's really fun to play with is try varying your activity and then looking at your sleep patterns. I can tell you for one that when Penny and I were out hiking 10 to 16 miles a day in the mountains of Italy last month that my heart rate variability, which I track on both the aura and the whoop band, went up, and it's not going to mean anything to you, to about 150 to 170 whereas my normal heart rate variability during normal working is maybe 70 to 90. And if I take the dogs, for instance, on a longer walk, say a three and a half mile walk instead of a two mile walk, lo and behold, my heart rate variability that next night will be much higher than if I took them on a two mile walk, which would be normal. The point of all this is there's very, very, very good strong evidence that the more heart rate variability you have, not only the longer you live, but the longer you live well. And so if for no other reason, start tracking your heart rate variability and start watching whether it corresponds to your activity level. And if it does talk about a motivator, let's get out there and walk. Okay, remember, why do we want to invest in these things? Well, just like you'd invest in your bank account or your stock market, this is the only house you're ever going to live in as far as we know. And so if we spent the amount of time and attention into taking care of this that we do to our homes, that we do to our bank account, that we do to our credit rating, imagine the possibilities. And that's how we got to approach this house. All right. Also, more and more insurance companies are now offering discounts or even free fitness trackers if you sign up for their health programs. So check that out too. And if you're a senior, a number of the Medicare programs now offer free gym memberships. They vary depending on what program you sign up for, but it's free. Take advantage of it. Okay, you can't afford a fitness tracker, I understand.  And even just journaling still works really well. Okay, next project for the new year. I've alluded to this already. Fill up on fiber. Did you know that 98% of Americans are walking around starving their gut buddies to death? That means that 98% of Americans are not getting enough fiber or prebiotic fiber into their diet. Now, did you know that just committing to supplement with fiber daily to make a dramatic impact on your overall health, you get the fiber of your choice. But please, please, please don't look for insoluble fiber. Insoluble fiber comes primarily from whole grains. And if you see grains as the source of fiber, like in wheat, like in oats, like in barley, like in rye, run the other way. That's not the fiber that your gut buddies want. Instead, they want soluble fiber. Soluble fiber is in root vegetables, for instance. It's in the chicory family of vegetables, like Belgian endive, like radicchio. Almost every grocery store now has that round red and white head of what looks like lettuce. That's radicchio. And it's fantastic. And believe me, your gut buddies think it's the best thing in the world.  Pressure-cooked lentils and pressure-cooked beans have very important fiber, and the pressure-cooking destroys the lectins, so they're safe. For instance, Thursday night I had, actually Wednesday night, Wednesday night I had pressure-cooked Bertoli beans from Jovial and Brussels sprouts and olive oil. That was my dinner. And I mixed it all together, and it was phenomenal, putting in a bunch of Tuscan Italian herbs on it and went to town. And that was dinner. Now, who benefited most? I can tell you, first of all, my taste buds were really happy, but more importantly, my gut buddies were so delighted that I can tell you that, and I've talked about this before, you should have seen the anaconda in the toilet bowl today. Magnificent. Terry Walls would be so impressed. Okay, that's what we're talking about, taking care of them. Now, Jerusalem artichokes, we're seeing those more and more in grocery stores. A great source of prebiotic fiber. And the prebiotic fibers and mushrooms actually strengthens your immune system. Garlic, onions, leeks, as you know, we feature leeks a lot. So get these guys into your diet.  And if you can just kind of follow that rule, you can make a huge change in your health in the coming year without making drastic steps. So that's it for today. That's a lot for you to chew on, so to speak. But taking these little steps, rather than saying I'm going to run a marathon this year or I'm going to the gym one hour a day this year, or you know that that's going to fail. Now for those of you who are going to do it, bless your heart, go for it. But the vast majority of Americans stop their resolution by February 5th. So this year, let's make some tiny changes. Tiny changes that we can live with. And you watch these tiny changes become slowly part of your routine. And it takes about six weeks to develop a new habit. But once that habit is developed, it's actually very hard to break. All right, that's your empowerment for 2022. Baby steps. That's all we're asking for. And feed your gut buddies what they need. And as you know, they'll take care of you. All right, it's time for our audience question from Bulletproof Coconut, based on the belly fat episode number 184. If you have a fair amount of body fat to lose, is it possible to gain muscle by working out on an extended fast? So is it technically possible for the body to recycle cellular proteins from other issues like fat cells and then use them to build muscle when given the stimulus on an extended fast? Hey, that's a great question. And in fact, yes, that's quite possible.  And protein, quite frankly, is not wasted. So you can absolutely borrow protein from other sources during an active exercise program, even if you're losing weight. One of the best recent examples that I've alluded to in the energy paradox and also in the upcoming Unlocking the Keto Code is the Italian cyclist experiment. They took well-trained Italian cyclists, put them on a training regimen of 12 weeks. And as you remember, they were all given the exact same food to eat. One group of cyclists ate their three meals over a 12-hour period. Just at 8 a.m., lunch at 1 p.m., finished dinner by 8 p.m., 12-hour eating window. The other group of cyclists got breakfast at 1 p.m. in the afternoon. Lunch at 4 p.m. in the afternoon had to finish dinner at 8, a 7-hour eating window. The cyclist who ate in the 7-hour window lost about 10 pounds during the three months, even though they ate the exact same amount of calories that the other group ate. The other group ate lost no weight. But what's really exciting is the cyclist who lost weight preserved and strengthened their muscle mass even though they were losing 10 pounds. So you're right. Even though they lost 10 pounds, they actually preserved and improved their muscle mass. And you're right. They just borrowed that protein from the other fat cells. So it's a great question. So you can absolutely build muscle during fasting. Great question. Now it's time for the review of the week from Apple Podcast, Haise Rea, or HaiseRE.  I have suffered from multiple autoimmune issues my whole life. When I was a child they went undiagnosed and it was just assumed the pain I felt was from various sports injuries. I did a primal diet nine years ago and really healed myself. I was amazed at the impact that food has on health. Unfortunately, being a vegetarian, the amount of time and effort needed to sustain wasn't possible with two small children. I'll never forget the healing power of food though. Now I have three children, was 50 pounds overweight and feeling sicker than ever. I knew something had to change and I found Dr. G. Six weeks in and I am already down 18 pounds and feel amazing. My joints are no longer swollen and I have so much energy. Dr. G. makes starting and following the plant paradox as a vegetarian or anyone so accessible and quick. I can do it with no stress and I could not be more grateful. Listening to the podcast while food prepping and cooking is now my favorite way to spend my me time. Keep up the great work. Well thank you very much, Hayes Rhee. Again, this is why I do this. Just yesterday in my office a young woman and her husband from Florida flew in to see me. She has a story very similar to you. Her autoimmune issues started as a young child. She was basically house ridden. When she stepped out doors she had to carry an EpiPen. She had so many things that she reacted to and they flew to see me because they actually got the plant paradox book. She followed it. She's now completely healed. She didn't carry her EpiPen anymore. Flew on an airplane and walked in to tell me how did you know that this would help me. Well, I can tell you I've been doing this now for over 20 years and you guys have taught me what works and what doesn't work and again the reason I show up every week here is to tell you what I know from you of what works and what doesn't work. And it's notes like this that I'm going to do this next year and the next year and as long as you'll have me we'll keep doing it. And we find out new exciting stuff from you guys every year and I'm still like a kid in a candy store because I can't go to the candy store anymore. So you're my candy store. So thanks a lot for writing again. Since the new year send us something on iTunes or YouTube and let us know what's going on with you. All right, everybody, this is Dr. Gundry. Have a great new year and I'll see you next year because I'm Dr. Gundry and I'm always looking out for you even in 2022. Before you go, I just wanted to remind you that you can find the show on iTunes, Google Play, Stitcher or wherever you get your podcasts. I'm Dr. Gundry and I'm always looking out for you.


I'm Lorraine, I'm the CEO of Glitter and Spice Accessories, Inc. We create teething accessories for the modern-day parent and their littles. Our experience with Small Business BC has been one of the best experiences we've had. From the awards to the seminars, it's been so valuable and useful. The marketing and the PR exposure we got from the awards was amazing and we also won the Premier's Prize of 1500 as well as the one-year all-access pass to Small Business BC's education resources such as the seminars and one-on-one business mentorships. As a small business owner, you wear so many different hats. I do marketing, sales, operations, product design, you name it. Oftentimes it's impossible to know every aspect of the business inside out and as a small business you don't always have the budget to hire professionals to handle these areas. So the seminars that Small Business BC provides allow you to, you know, learn all these different areas of the business and help you grow your business better. My favorite thing about being a small business owner is definitely the community. The support and love that we have received is it's just it leaves me speechless and especially Small Business BC it's the amount of resources and tools and how much they've helped us is just it's amazing.


What is a pest?  No. A pest is an organism that eats or damages crop plants that are grown in agriculture. For example, slugs eating lettuce in my garden. Pests can be controlled using pesticides or by introducing other species also known as biological control. Pesticides are natural or synthetic chemicals that are sprayed on crops. They include insecticides that control insects, fungicides that control fungi and herbicides that control weeds other plants that are not supposed to grow with the crop. Pesticides can be sprayed all over a field using industrial machinery like tractors or planes or in specific areas by humans. Why do you think this person is wearing so much protection while spraying crops with pesticides? Pesticides can be toxic to other organisms, not just the organisms they aim to control. One example of this is DDT. DDT was used as an insecticide in the mid 20th century to control mosquitoes which spread malaria, a potentially fatal disease amongst humans. However, DDT was found to accumulate in food chains, also known as bioaccumulation. The molecules of DDT did not break down, but instead were stored in the bodies of organisms and passed on when those organisms were eaten. The DDT became more concentrated in the higher levels of the food chain. The effect on birds of prey was catastrophic. It caused them to lay eggs with thin shells that broke easily when the birds incubated them. This led to a fall in the birthrate of birds of prey and a massive decrease in their populations. The use of DDT was eventually banned as a result. Through the process of evolution and natural selection, some pests become resistant to pesticides. They survive and reproduce. The overuse of pesticides has led to an increasing number of those organisms, which means that some pesticides are now useless. Apart from using pesticides, which is also known as chemical control, farmers can use biological control mechanisms. Can you think what that could be? Biological control is the use of a natural predator of the organism you wish to control. For example, aphids or green flies are herbivores that feed on many human crops. Aphids are eaten by lady birds. Lady birds could be added to an environment to control the number of aphid pests. Sometimes this method of control works well. However, the introduction of a new species to an environment can have unexpected effects. One example of this, the Hawaiian cane toad. It was introduced in Australia to control the population of the cane beetle, a pest that damaged sugar cane crops. However, the Hawaiian cane toad had little effect on the cane beetle population. Instead, cane toads reproduced rapidly and spread across Australia as they had no natural predators. It's now considered a pest species itself and has been blamed for the extinction of some endemic species. So now you know how pests can be controlled to allow farmers to grow the maximum amount of crops possible. If you want to learn more about intensive farming methods and why they use pesticides and fertilisers, watch this video. If you like the video, give it a thumbs up and don't forget to subscribe, comment below if you have any questions. Why not check out our Fuse School app as well? Until next time.