The advent of field-programmable gate arrays (FPGAs) in the 1980s revolutionized electronic design. Some 40 years later, field-programmable qubit arrays (FPQAs) promise to spark a similar revolution in circuit design for quantum computing.
The advent of field-programmable gate arrays (FPGAs) in the 1980s revolutionized electronic design. FPGAs allow designers to create custom logic circuits tailored to specific applications and quickly prototype and test new designs before committing to expensive ASIC development.
Some 40 years later, the field-programmable qubit array (FPQA) promises to spark a similar revolution in quantum computing circuit design.
FPQA can assist quantum algorithm designers to adjust the layout of the quantum processor according to their needs, optimizing the connection of qubits to achieve the best performance for a given problem. To match the algorithm, FPQA allows users to dynamically create quantum processors.
Why is FPQA needed?
Quantum phenomena known as superposition, entanglement and interference are key to the power of quantum computers.
To take advantage of these phenomena, qubits need to interact through quantum gates (such as quantum CNOT two-qubit gates) or by exploiting other types of interactions that depend on the distance between qubits (such as Rydberg interactions).
Typically, quantum computer designers refer to "connectivity" as a way of describing which qubits can interact with other qubits.
Key quantum resources such as the number of qubits and the number of quantum gates are very scarce. It is because of this scarcity that it is necessary to optimize the deployment of these limited resources in the computing process.
In many static designs, if qubits that are far apart from each other need to interact, the solution is to perform a series of "qubit swaps" that bring the information carried in the target qubit closer together. .
But these qubit swaps both tie up quantum resources and introduce new sources of error. The ability to dynamically change the position of the qubits provides the flexibility to map problems into the physical arrangement of qubits, which can help designers to code more efficiently with the help of geometric arrangements of qubits, thus using fewer resources to solve question.
What is FPQA?
In order to match the requirements of the algorithm, FPQA allows users to dynamically create quantum processors. Designers can program the qubit connections based on user-specified geometric positions of the qubits relative to each other. The concept arose from several academic laboratories, including Harvard University, where researchers have successfully demonstrated FPQA based on neutral-atom quantum techniques with both analog and digital capabilities.
How does FPQA work?
FPQA is implemented using a unique control mechanism used in neutral-atom quantum computers. The processor layout of a neutral-atom quantum computer is achieved by trapping a neutral atom (such as rubidium-87) with a focused laser beam (sometimes called optical tweezers).
By changing where each laser is pointed, the user can rearrange the atoms in space, enabling the programmability of the qubit connections.
The geometry of the atoms can now be updated at the start of each computation. In the future, it will be possible to realize dynamic architectures with information buses by moving atoms around during computation, such as the recently demonstrated configuration.
This development will be key to optimizing the ratio of control signals to the number of qubits and enabling arbitrary connections between qubits beyond geometric constraints.
How FPQA can more effectively solve a range of problems
FPQA improves the resource efficiency of quantum algorithms by reducing qubit and gate overhead. The ability to quickly update qubit layouts and connections enables rapid testing, benchmarking and optimization of algorithms by providing custom operations for each operation.
optimization
Optimization is an example of how to achieve higher quantum computing performance with FPQA. Many optimization problems can be mathematically described in the form of graphs, each node is used to describe the variables in the optimization problem, and each edge can represent various relationships between them.
For example, nodes can describe the potential locations of numerous 5G towers, while edges describe pairs of towers that cannot operate simultaneously without creating interference. In another, more abstract description, imagine each node as a stock, and an edge between two nodes indicates that these stocks are related.
By assigning each node to a qubit and setting up the connections, these graphs can be mapped onto analog FPQA such that two qubits can interact when the corresponding atom has an edge, thus effectively finding A solution that has as many qubits as there are variables in the problem.
Other promising areas for quantum optimization with FPQA include robotics, wiring optimization, and protein design. In all of these examples, the geometry of the problems and their constraints make them challenging for typical computers.
quantum simulation
Another important use case for FPQA is quantum simulations, where quantum computers can be used to gain insight into complex phenomena in important quantum mechanical systems, such as new materials. Certain physical phenomena in materials can only be understood by exploring the interactions between atoms.
In order to observe these phenomena, it is necessary to simulate this pattern, which can be achieved by using FPQA to properly arrange the qubits. Similar applications can also be found in materials science and high-energy physics.
Dynamic performance optimization
FPQA can not only create a customized computer for each application, but even update the computer before starting each step in the calculation process. This opens the door for automated processor refreshes to further improve processing performance.
It can also optimize dynamic problems, for example, one can dynamically solve the instantaneously changing path problem of an autonomous robot, which can update the position of the qubit in time when the conditions leading to the accident change, even if it needs to be adopted. The same should be true for scheduled paths.
In cases where the quantum processor (the core of a quantum computer) is not intended to be used as a general-purpose processor, but is optimized for a specific problem, FPQA allows continuous experimentation during the design process before finalizing the processor's layout.
FPQA holds the key to more efficient use of quantum resources, thereby accelerating the path to practical quantum computers.