It is widely believed that quantum computing is on the threshold of practicality, with performance that will soon greatly surpass that of classical computing. On the contrary, I argue that quantum computing does not currently exist, and probably never will. First, although quantum annealing systems have been demonstrated to solve practical optimization problems, they are actually performing classical analog annealing, with no quantum enhancement. In contrast, while systems of quantum gate arrays, which are expected to perform digital quantum computing, have been fabricated with up to ~ 100 qubits in several technologies, they have not performed any practical computations. This is not merely a question of excess noise; the theory of massive quantum entanglement, necessary for the desired performance, has never been actually been verified. The well-established quantum results such as electronic energy bands do not incorporate quantum entanglement. I suggest that the experimental observations in multi-qubit systems may be explained as the result of delocalized coupled oscillator modes, similar to that in electronic energy bands. Such coupled modes would not yield the exponential increase in degrees of freedom needed for quantum speedup, and hence would not be useful for computing. Tests on these multi-qubit systems should be able to distinguish these two models. The quantum computing research community really needs to address this issue.
It is widely believed that quantum computing is on the threshold of practicality, with performance that will soon greatly surpass that of classical computing. On the contrary, I argue that quantum computing does not currently exist, and probably never will. First, although quantum annealing systems have been demonstrated to solve practical optimization problems, they are actually performing classical analog annealing, with no quantum enhancement. In contrast, while systems of quantum gate arrays, which are expected to perform digital quantum computing, have been fabricated with up to ~ 100 qubits in several technologies, they have not performed any practical computations. This is not merely a question of excess noise; the theory of massive quantum entanglement, necessary for the desired performance, has never been actually been verified. The well-established quantum results such as electronic energy bands do not incorporate quantum entanglement. I suggest that the experimental observations in multi-qubit systems may be explained as the result of delocalized coupled oscillator modes, similar to that in electronic energy bands. Such coupled modes would not yield the exponential increase in degrees of freedom needed for quantum speedup, and hence would not be useful for computing. Tests on these multi-qubit systems should be able to distinguish these two models. The quantum computing research community really needs to address this issue.
Quantum computing promises computational performance that is exponentially faster than any conceivable classical computer. This is due to the theoretically expected scaling of N entangled qubits, with parallel evolution of 2^N quantum states. This is in sharp contrast to classical computing, where N bits may have 2^N classical states, but only one at a time. It is widely believed that quantum superposition and entanglement have been demonstrated in several experimental systems, and that practical quantum computing can be achieved once sufficiently long quantum relaxation times are obtained. On the contrary, we suggest that there may be serious problems with quantum computing on both the macroscopic and microscopic levels, and that the experiments thus far have not proven the existence of non-classical superposition states, which are necessary for the proper functioning of qubits. In order to investigate this further, we propose new experiments in three physical systems: electron spins, single photons, and superconducting loops. We further suggest that certain more limited classes of quantum computing, such as quantum annealing, do not require quantum entanglement, and can achieve significant performance enhancements even if universal quantum computing proves to be impossible.
Last chapter, we talked about whether quantum states should be thought of as exponentially long vectors, and I brought up class BQP/qpoly and concepts like quantum advice. Actually, I’d say that the main reason why I care is something I didn't mention last time, which is that it relates to whether we should expect quantum computing to be fundamentally possible or not. There are people, like Leonid Levin and Oded Goldreich, who just take it as obvious that quantum computing must be impossible. Part of their argument is that it's extravagant to imagine a world where describing the state of 200 particles takes more bits then there are particles in the universe. To them, this is a clear indication something is going to break down. So part of the reason that I like to study the power of quantum proofs and quantum advice is that it helps us answer the question of whether we really should think of a quantum state as encoding an exponential amount of information. So, on to the Eleven Objections. Works on paper, not in practice. Violates Extended Church–Turing Thesis. Not enough “real physics.” Small amplitudes are unphysical. Exponentially large states are unphysical. Quantum computers are just souped-up analog computers. Quantum computers aren't like anything we've ever seen before. Quantum mechanics is just an approximation to some deeper theory. Decoherence will always be worse than the fault-tolerance threshold. We don't need fault-tolerance for classical computers. Errors aren't independent. What I did is to write out every skeptical argument against the possibility of quantum computing that I could think of. We'll just go through them, and make commentary along the way. Let me just start by saying that my point of view has always been rather simple: it's entirely conceivable that quantum computing is impossible for some fundamental reason. If so, then that's by far the most exciting thing that could happen for us. That would be much more interesting than if quantum computing were possible, because it changes our understanding of physics. To have a quantum computer capable of factoring 10000-digit integers is the relatively boring outcome – the outcome that we'd expect based on the theories we already have.
An ultimate goal of quantum computing is to perform calculations beyond the reach of any classical computer.It is therefore imperative that useful quantum computers be very difficult to simulate classically, otherwise classical computers could be used for the applications envisioned for the quantum ones.Perfect quantum computers are unarguably exponentially difficult to simulate: the classical resources required grow exponentially with the number of qubits N or the depth D of the circuit.This difficulty has triggered recent experiments on deep, random circuits that aim to demonstrate that quantum devices may already perform tasks beyond the reach of classical computing.These real quantum computing devices, however, suffer from many sources of decoherence and imprecision which limit the degree of entanglement that can actually be reached to a fraction of its theoretical maximum.They are characterized by an exponentially decaying fidelity F ∼ ð1 -ϵÞ ND with an error rate ϵ per operation as small as ≈1% for current devices with several dozen qubits or even smaller for smaller devices.In this work, we provide new insight on the computing capabilities of real quantum computers by demonstrating that they can be simulated at a tiny fraction of the cost that would be needed for a perfect quantum computer.Our algorithms compress the representations of quantum wave functions using matrix product states, which are able to capture states with low to moderate entanglement very accurately.This compression introduces a finite error rate ϵ so that the algorithms closely mimic the behavior of real quantum computing devices.The computing time of our algorithm increases only linearly with N and D in sharp contrast with exact simulation algorithms.We illustrate our algorithms with simulations of random circuits for qubits connected in both one-and twodimensional lattices.We find that ϵ can be decreased at a polynomial cost in computing power down to a minimum error ϵ ∞ .Getting below ϵ ∞ requires computing resources that increase exponentially with ϵ ∞ =ϵ.For a two-dimensional array of N ¼ 54 qubits and a circuit with control-Z gates, error rates better than state-of-the-art devices can be obtained on a laptop in a few hours.For more complex gates such as a SWAP gate followed by a controlled rotation, the error rate increases by a factor 3 for similar computing time.Our results suggest that, despite the high fidelity reached by quantum devices, only a tiny fraction ð∼10 -8 Þ of the system Hilbert space is actually being exploited.
Recent advances in quantum technology have led to the development and manufacturing of experimental programmable quantum annealing optimizers that contain hundreds of quantum bits. These optimizers, commonly referred to as 'D-Wave' chips, promise to solve practical optimization problems potentially faster than conventional 'classical' computers. Attempts to quantify the quantum nature of these chips have been met with both excitement and skepticism but have also brought up numerous fundamental questions pertaining to the distinguishability of experimental quantum annealers from their classical thermal counterparts. Inspired by recent results in spinglass theory that recognize 'temperature chaos' as the underlying mechanism responsible for the computational intractability of hard optimization problems, we devise a general method to quantify the performance of quantum annealers on optimization problems suffering from varying degrees of temperature chaos: A superior performance of quantum annealers over classical algorithms on these may allude to the role that quantum effects play in providing speedup. We utilize our method to experimentally study the D-Wave Two chip on different temperature-chaotic problems and find, surprisingly, that its performance scales unfavorably as compared to several analogous classical algorithms. We detect, quantify and discuss several purely classical effects that possibly mask the quantum behavior of the chip.
Quantum computing is widely promoted as a transformative technology with the potential to revolutionize fields as diverse as artificial intelligence, finance, optimization, and materials science. Yet the public narrative surrounding quantum computing often diverges sharply from the technical realities acknowledged by specialists. This paper examines the roots of this discrepancy by distinguishing the genuine scientific achievements of quantum information theory from the speculative expectations projected onto quantum hardware. After outlining the central practical challenges posed by noise, decoherence, and the extreme overhead of quantum error correction, we argue that the path to large-scale, general-purpose quantum computers remains uncertain and potentially infeasible. Drawing a historical parallel with the nuclear fusion project, we illustrate how a theoretically elegant idea may confront fundamental obstacles in physical implementation. We further analyze the sources of technological hype in both scientific and popular discourse and highlight more realistic avenues for progress, such as quantum simulation and quantum-inspired classical computation. Finally, we revisit the conceptual foundations of quantum computational advantage and emphasize that constructive probability interference - rather than superposition or entanglement alone - may provide the true insight into quantum speedups. The goal of this note is not to diminish the value of quantum information research but to restore balance between promise and feasibility, clarifying what quantum computers can and cannot achieve in practice.
The concept of quantum computing has arisen as a methodology by which very rapid computations can be achieved. There also has been considerable discussion about physical implementations of the qubit. This has led, in recent years, to a situation in which quantum computing and quantum information theory are being rapidly developed. In general, the specific advantages offered by quantum computing have been somewhat nebulous. On the one hand, faster computing was promised, but we now know that no speedup of most algorithms exists relative the speed that can be obtained with massive parallel processing. Then, we are promised that the use of entanglement will make quantum computing possible with a much smaller use of resources. Yet, entanglement must be viewed as a hidden variable, which is not accessible in experiment. How does this provide the speedup? We have suggested that analog processing may provide a suitable alternative, and may be the basis which provides the speedup in quantum computing, but this is a controversial assertion. In this talk, we will discuss these particular viewpoints, along with several approaches to a wave basis for (quantum) computing.