驱动耗散量子电路。 图片来源:Nature Physics (2023)。 DOI: 10.1038/s41567-023-02199-w
The researchers, with support from the Department of Energy's Quantum Computing User Program (QCUP) at the Department of Energy's Oak Ridge National Laboratory, simulated a key quantum state at the largest scale.
The technology used by the team could help develop quantum simulation capabilities for the next generation of quantum computers.
The study used Quantinuum's H1-1 computer to model a quantum version of the classical mathematical model that tracks how the disease spreads. Time on the computer is provided by QCUP, part of the Oak Ridge Leadership Computing Facility, which provides time to private quantum processors across the country to support research projects.
The model uses qubits or qubits to simulate the transition between an active state, such as an infection, and an inactive state, such as death or recovery.
"The goal of this research is to work on building features on quantum computers to solve this problem and other similar problems that are difficult to compute on traditional computers," said Andrew Porter, co-author of the study and assistant professor of physics at the University of British Columbia in Vancouver.
"This experiment simulates trying to lead a quantum system to a specific state while competing with quantum fluctuations away from that state. There is a transition point where these competing effects are perfectly balanced. This point separates the stages of transition to success and failure.
The farther the system is from equilibrium, the more likely it is that the classic version of the model will collapse due to the size and complexity of the equations. The team sought to use quantum computing to simulate these dynamics.
Classic computers store information in bits equal to 0 or 1. In other words, classic bits, such as light switches, exist in one of two states: on or off. This binary dynamics are not necessarily suitable for modeling transition states, such as those studied in disease models.
Quantum computing uses the laws of quantum mechanics to store information in qubits, which are equivalent to qubits. Qubits can exist in multiple states at the same time through quantum superposition, which allows qubits to carry more information than classical bits.
In a quantum superposition state, a qubit can exist in two states at the same time, similar to a spinning coin – the coin has neither heads nor tails, and the qubit has neither a frequency nor another frequency. The value of measuring a qubit determines the probability of measuring any of the two possible values, similar to stopping a coin in heads or tails. This dynamic allows for a wider range of possible values that can be used to study complex problems such as transitional states.
"The goal of this research is to work on building features on quantum computers to solve this problem and other similar problems that are difficult to compute on traditional computers," said Andrew Porter, co-author of the study and assistant professor of physics at the University of British Columbia in Vancouver.
"This experiment simulates trying to lead a quantum system to a specific state while competing with quantum fluctuations away from that state. There is a transition point where these competing effects are perfectly balanced. This point separates the stages of transition to success and failure.
The farther the system is from equilibrium, the more likely it is that the classic version of the model will collapse due to the size and complexity of the equations. The team sought to use quantum computing to simulate these dynamics.
Classic computers store information in bits equal to 0 or 1. In other words, classic bits, such as light switches, exist in one of two states: on or off. This binary dynamics are not necessarily suitable for modeling transition states, such as those studied in disease models.
Quantum computing uses the laws of quantum mechanics to store information in qubits, which are equivalent to qubits. Qubits can exist in multiple states at the same time through quantum superposition, which allows qubits to carry more information than classical bits.
In a quantum superposition state, a qubit can exist in two states at the same time, similar to a spinning coin – the coin has neither heads nor tails, and the qubit has neither a frequency nor another frequency. The value of measuring a qubit determines the probability of measuring any of the two possible values, similar to stopping a coin in heads or tails. This dynamic allows for a wider range of possible values that can be used to study complex problems such as transitional states.
The researchers hope that these possibilities will drive a quantum revolution that will allow quantum computers to surpass classical machines in speed and power. However, the qubits used in current quantum machines tend to degenerate easily. This attenuation results in a high error rate, which can confound the results of any model that is larger than the test problem.
Potter and his colleagues obtained the time with QCUP on a Quantinuum computer that uses captured ions as qubits. They measured circuits, or quantum gates, throughout the run, and used a technique called qubit recycling to eliminate degraded qubits.
"We use quantum processors to simulate a system in which active qubits are able to activate adjacent qubits or become inactive," Porter said. "By monitoring the system in real-time at every step and testing as we do it, we can detect the likelihood that executing a quantum gate on a qubit could affect the state of the qubit, and if not, remove it from the computation. In this way, we avoid the opportunity for mistakes to creep in.
The team determined that they could use their method to control errors on 20 qubits and simulate a quantum system that was almost four times this size. They estimate that at 70 qubits, their approach could be equal to or exceed the capabilities of classical computers.
"This is the first time this method has been used for a system of this scale," Porter said.
The next steps include applying qubit recycling to quantum problems, such as simulating the properties of materials and calculating their lowest energy state or quantum ground state.
The paper was published in the journal Nature Physics.
More information: Eli Chertkov et al., Characterizing non-equilibrium phase transitions on quantum computers, Nature Physics (2023). DOI： 10.1038/s41567-023-02199-w
期刊信息： Nature Physics