Insider Brief<\/p>\n
Quantum Elements<\/a> and USC published a peer-reviewed Quantum Monte Carlo algorithm in Physical Review Letters that provides a more efficient classical method for simulating noisy quantum circuits.<\/p>\n The algorithm compresses noisy quantum-circuit simulations while preserving dynamics relevant to quantum error correction, correlated noise and decoder performance.<\/p>\n Quantum Elements<\/a> said the method supports its development of quantum error-correction digital twins, including an AWS collaboration that simulated a 97-physical-qubit surface-code syndrome-extraction round on classical computing infrastructure.<\/p>\n PRESS RELEASE \u2014 \u00a0Quantum Elements<\/a>\u00a0and USC<\/a> today announced the publication of a new Quantum Monte Carlo algorithm in\u00a0Physical Review Letters, providing a more efficient way to simulate noisy quantum circuits on classical computers and supporting the company\u2019s development of digital twins for quantum error correction.\u00a0<\/p>\n The peer-reviewed paper,\u00a0Real-Time Sign-Problem-Suppressed Quantum Monte Carlo Algorithm for Noisy Quantum Circuit Simulations<\/a>,\u00a0was co-authored by Dr. Tong Shen, quantum research scientist at Quantum Elements<\/a> and a postdoctoral researcher at USC, and USC Professor Daniel A. Lidar, co-founder and Chief Scientific Officer of Quantum Elements.\u00a0<\/p>\n While quantum processors are becoming increasingly sophisticated, they are still affected by environmental noise, crosstalk between qubits, and control imperfections. These effects are obstacles to developing fault-tolerant quantum computers. To study this behavior, researchers often simulate quantum systems on classical computers. One way to do this is direct density-matrix simulation, which tracks a noisy quantum system, including both its quantum state and its interaction with the environment. However, the amount of information\u00a0required\u00a0to\u00a0represent\u00a0the system becomes prohibitive as qubit counts grow. \u00a0<\/p>\n The Quantum Monte Carlo algorithm presented by Quantum Elements<\/a> in PRL compresses the simulation, which allows researchers to model noisy quantum-circuit behavior with lower computational resources while preserving the dynamics needed to study quantum error correction, correlated noise, and decoder performance.\u00a0<\/p>\n \u201cFault tolerance will require a much tighter feedback loop between hardware, control, simulation and decoding,\u201d said Izhar Medalsy, co-founder and CEO of Quantum Elements<\/a>. \u201cThis gives us a rigorous algorithmic foundation for building digital twins that capture the noise behavior hardware teams need.\u201d\u00a0<\/p>\n The practical application of this method was\u00a0demonstrated\u00a0in\u00a0an\u00a0AWS collaboration<\/a>\u00a0with Quantum Elements<\/a>, USC, and Harvard, where researchers used a Quantum Monte Carlo-accelerated digital twin to simulate a 97-physical-qubit, distance-7 surface-code syndrome-extraction round on classical high-performance computing infrastructure. AWS reported that a brute force, full open-system simulation of the same system would require tracking 497\u00a0density-matrix entries, while the QMC-based method ran in about an hour on a single compute node.\u201d\u00a0AWS helped translate the peer-reviewed\u00a0methodology\u00a0into an AWS architecture,\u00a0leveraging\u00a0AWS\u00a0ParallelCluster\u00a0to deploy the Quantum Elements digital twin as a containerized workload.\u00a0The solution scales horizontally across multiple instances, making it well suited for\u00a0a large number of\u00a0qubits.\u00a0<\/p>\n \u201cThis is peer-reviewed evidence of what we\u00a0demonstrated\u00a0earlier this year in collaboration with Quantum Elements<\/a>, and we look forward to\u00a0leveraging\u00a0AWS<\/a>\u2019 classical and quantum compute resources in conjunction with their digital twin technology to accelerate the path towards fault tolerance via quantum error correction,\u201d said Michael Brett,\u00a0Worldwide Go-To-Market Strategy Lead for Quantum Technologies at AWS.\u00a0<\/p>\n The result comes as quantum hardware teams increasingly focus on quantum error correction as the route to useful, fault-tolerant systems. As devices scale, the engineering challenge lies in understanding how real noise affects logical performance and how software, controls, and decoding can compensate for it.\u00a0<\/p>\n