The D-Wave Advantage Quantum Computer
The Advantage™ quantum system is the first and only quantum computer that enables customers to develop and run in-production hybrid quantum applications. Access to Advantage is through Leap, the quantum cloud service built for business.
The Advantage quantum computer leverages quantum dynamics to accelerate and enable new methods for solving discrete optimization, sampling, machine learning, and other complex problems. Advantage is the most powerful and connected quantum computer in the world. It features a totally new processor architecture with 5,000+ qubits, 2.5x more qubit connectivity, and the ability to run larger, more complex problems.
For the first time, quantum applications are in-production, using the powerful combination of Advantage and D-Wave’s hybrid solver service. The hybrid solver service is also available through Leap, and can run problems with up to 1,000,000 variables on general problems, and up to 20,000 variables on dense problems. Whether dense or sparse, it delivers excellent solutions to complex problems, often out-performing classical approaches.
For quantum computing, as for classical, solving a problem requires that it be formulated in a way the computer and its software understand. D-Wave’s Ocean software development kit includes a suite of open-source Python tools on the D-Wave GitHub repository for solving hard problems with quantum computers. The software stack implements the computations needed to transform an arbitrarily posed problem to a form solvable on a quantum solver.
The Ocean software fits between applications and the compute resources:
- Application: Original problem in its context (the “problem space”) including application data and a clearly defined goal. For example, a circuit-fault diagnosis application in chip manufacturing requires the identification of the minimum set of failed logic gates in a circuit.
- Mapping Methods: Tools that translate the application goal and data into a problem form suitable for quantum computing. They also receive solution samples and translate them back into solutions for the application layer. For example, dwave_networkx helps map structural imbalance analysis into a BQM.
- Uniform Sampler API: Abstraction layer that represents the problem in a form that can access the selected sampler.
- Samplers: Tools that receive a problem in the form of a BQM and return solution samples. Ocean implements several samplers that use the D-Wave QPU as well as classical compute resources. You can use Ocean tools to customize a D-Wave sampler, create your own, or use existing classical ones.
- Compute Resources: The processing hardware on which the problem is solved. This might be a D-Wave QPU but it may also be the CPU of your laptop computer.
While users can submit problems to the system in a number of different ways, ultimately a problem represents a set of values that correspond to the weights of the qubits and the strength of the couplers. The system takes these values along with other user-specified parameters and sends a single QMI to the QPU. Problem solutions correspond to the optimal configuration of qubits found; that is, the lowest points in the energy landscape. These values are returned to the user program over the network.
Because quantum computers are probabilistic rather than deterministic, multiple values can be returned, providing not only the best solution found, but also other very good alternatives from which to choose. Users can specify the number of solutions they want the system to return.
The D-Wave Advantage system also gives users important control over the quantum computation:
- Virtual graphs: Many optimization and machine learning algorithms are commonly described as graph problems. D-Wave’s virtual graphs feature improves accuracy in the upgraded system, by allowing control over the interaction of groups of qubits, to model a node or link in a complex graph.
- Pause and Quench: In the standard application of quantum annealing in D-Wave systems, qubits evolve according to a predetermined anneal schedule. Some types of research (e.g., quantum simulation), however, may benefit from fine-grained adjustments to the default schedule. In these cases, you can change the shape of the energy waveform by introducing a pause or quench (i.e., abrupt termination). This level of control helps investigate what is happening partway through the annealing process.
- Reverse annealing: This lets users program the system in an entirely new way, harnessing powerful heuristic search algorithms for optimization and machine learning, and applications such as cybersecurity and drug discovery. Reverse annealing allows users to specify the problem they wish to solve along with a predicted solution in order to narrow the search space for the computation.
- Anneal offsets: Certain problems benefit when some qubits anneal slightly before or after others. The anneal offsets feature lets users advance or delay anneal paths to enhance application performance. Algorithms using this feature have shown performance improvements of up to 1000 times for some problem types.