Physicists for Computational Quantum Physics at the Simons Foundation’s Flatiron Institute have successfully simulated the dynamics of a system composed of hundreds of interacting ‘qubits’ using classical computation, challenging a recent claim. Adapting a mathematical algorithm from the 1980s and applying it to modern tensor networks, the team demonstrated that a problem previously thought to require a quantum computer could be solved with conventional hardware; the simulation ran efficiently enough to be completed on a personal laptop. “We at the CCQ are always skeptical of these kinds of claims,” says Joseph Tindall, an associate research scientist and first author on the new Science paper. This breakthrough, reported May 21, opens new research avenues for quantum dynamics and offers a powerful methodology for tackling complex optimization problems.
Tensor Networks Challenge ‘Quantum Supremacy’ Claim
The team’s methodology demonstrates that classical computers, equipped with sophisticated mathematical tools, can tackle problems once thought intractable, opening new avenues for research in quantum dynamics and optimization. The breakthrough centers on simulating the behavior of qubits, the quantum equivalent of classical bits, arranged in complex lattices. While qubits’ ability to exist in multiple states simultaneously complicates simulations, the CCQ team employed tensor networks, which Joseph Tindall describes as “a zip file for the wave function,” to compress the vast amount of data required. This compression allowed Tindall to perform initial calculations on a standard laptop using ITensor, a high-performance software library developed at the CCQ. The simulations published in Science matched theoretical predictions and confirmed the results of a recent quantum computing study, without requiring any quantum hardware. The team also revived a 1980s algorithm, belief propagation, adapting it for modern quantum systems.
Miles Stoudenmire explains that this approach is approximate but “way cheaper” and allows them to address significantly larger problems than previously possible with more sophisticated methods. “We could have picked some more arbitrary target,” Stoudenmire adds, “but it was logical to pick this one that has a big claim attached to it.” This success highlights the ongoing synergy between classical and quantum computing, with insights from both fields informing and accelerating progress in the other.
Adapting 1980s Belief Propagation for Quantum Dynamics
This feat is notable given the inherent difficulty in simulating qubits, which, unlike classical bits, can exist in multiple states simultaneously, exponentially increasing computational demands. Central to this breakthrough was the revival and adaptation of a 1980s algorithm known as belief propagation, applied to modern tensor networks; these networks function as a sophisticated compression method for the wave function describing the quantum system, effectively reducing the computational burden. Miles Stoudenmire, a research scientist at the CCQ, further emphasized the algorithm’s practicality, noting that “It’s a little more approximate than some of the other methods, but it’s way cheaper, and we can run it much more directly on lots of harder problems.” The simulations not only matched theoretical predictions but also validated the results obtained by a recent quantum computing experiment, all without requiring a quantum computer.
But it was like ‘Why not pick this one that has a big claim attached to it?’” The work was particularly challenging due to quantum entanglement, which means the qubits can’t be treated individually, even when they’re far apart.
ITensor Software Enables High-Accuracy Simulations
The Center for Computational Quantum Physics (CCQ) has significantly expanded the capabilities of classical computation through advancements in software, specifically the ITensor library. Researchers leveraged ITensor to successfully simulate the behavior of a quantum system comprising hundreds of interacting ‘qubits’, a scale previously considered the exclusive domain of quantum computers; this achievement challenges the notion of a definitive advantage in computational power. The team did not simply invent new algorithms; they revived belief propagation, an algorithm from the 1980s, adapting it for use with modern tensor networks and quantum systems. This demonstrates the potential for ITensor and similar software to serve as a valuable tool for both classical and quantum computing communities, offering a pathway for verifying quantum computations and guiding future development. “That can help guide us, and it can also help guide quantum computing researchers,” Tindall notes, “because we don’t have to build a quantum computer.”
That can help guide us, and it can also help guide quantum computing researchers, because, obviously, the barrier for entry for us to simulate certain things is a lot easier than for them, because we don’t have to build a quantum computer.
Simulating Qubit Systems with Three-Dimensional Tensor Networks
This achievement hinges on the innovative application of three-dimensional tensor networks, a technique that efficiently compresses the vast amount of data required to represent quantum states. The simulations, capturing three-dimensional dynamics, represent a significant step forward in a field where working with these complex mathematical objects “is a software engineering challenge in itself.” This methodology not only validates classical computation’s continued relevance but also offers a valuable tool for guiding the development of quantum computing itself, as classical simulations can help identify promising avenues for quantum algorithm design.
The good side of the classical versus quantum computing debate is that there’s a lot of synergy between the kind of simulations we’re interested in and the codes we write and what can be realized on these quantum computers.
