June 14, 2024by Henning Soller and Niko Mohr with Elisa Becker-Foss, Kamalika Dutta, Martina Gschwendtner, Mena Issler, and Ming Xu <\/p>\n
Quantum computing can leverage the states of entangled qubits1 to solve problems that classical computing cannot currently solve and to substantially improve existing solutions. These qubits, which are typically constructed from photons, atoms, or ions, can only be manipulated using specially engineered signals with precisely controlled energy that is barely above that of a vacuum and that changes within nanoseconds. This control system for qubits, referred to as quantum control, is a critical enabler of quantum computing because it ensures quantum algorithms perform with optimal efficiency and effectiveness. <\/p>\n
While the performance and scaling limitations of current quantum control systems preclude large-scale quantum computing, several promising technological innovations may soon offer scalable control solutions. <\/p>\n
A modern quantum computer comprises various hardware and software components, including quantum control components that require extensive space and span meters. In quantum systems, qubits interact with the environment, causing decoherence and decay of the encoded quantum information. Quantum gates (building blocks of quantum circuits) cannot be implemented perfectly at the physical system level, resulting in accumulated noise. Noise leads to decoherence, which lowers qubits superposition and entanglement properties. Quantum control minimizes the quantum noisefor example, thermal fluctuations and electromagnetic interferencecaused by the interaction between the quantum hardware and its surroundings. Quantum control also addresses noise by improving the physical isolation of qubits, using precise control techniques, and implementing quantum error correction codes. Control electronics use signals from the classical world to provide instructions for qubits, while readout electronics measure qubit states and transmit that information back to the classical world. Thus, the control layer in a quantum technology stack is often referred to as the interface between the quantum and classical worlds. <\/p>\n
Components of the control layer include the following: <\/p>\n
A superconducting- or spin qubitbased computer, for example, includes physical components such as quantum chips, cryogenics (cooling electronics), and control and readout electronics. <\/p>\n
Quantum computing requires precise control of qubits and manipulation of physical systems. This control is achieved via signals generated by microwaves, lasers, and optical fields or other techniques that support the underlying qubit type. A tailored quantum control system is needed to achieve optimal algorithm performance. <\/p>\n
In the context of a quantum computing stack, control typically refers to the hardware and software system that connects to the qubits the application software uses to solve real-world problems such as optimization and simulation (Exhibit 1). <\/p>\n
At the top of the stack, software layers translate real-world problems into executable instructions for manipulating qubits. The software layer typically includes middleware (such as a quantum transpiler2) and control software comprising low-level system software that provides compilation, instrument control, signal generation, qubit calibration, and dynamical error suppression.3 Below the software layer is the hardware layer, where high-speed electronics and physical components work together to send signals to and read signals from qubits and to protect qubits from noise. This is the layer where quantum control instructions are executed. <\/p>\n
Quantum control hardware systems are highly specialized to accommodate the intricacies of qubits. Control hardware interfaces directly with qubits, generating and reading out extremely weak and rapidly changing electromagnetic signals that interact with qubits. To keep qubits functioning for as long as possible, control hardware systems must be capable of adapting in real time to stabilize the qubit state (feedback calibration) and correct qubits from decaying to a completely decoherent state4 (quantum error correction). <\/p>\n
Although all based on similar fundamental principles of quantum control, quantum control hardware can differ widely depending on the qubit technology with which it is designed to be used (Exhibit 2). <\/p>\n
For example, photonic qubits operate at optical frequencies (similar to fiber internet), while superconducting qubits operate at microwave frequencies (similar to a fifth-generation network). Different types of hardware using laser technology or electronic circuits are needed to generate, manipulate, and transmit signals to and from these different qubit types. Additional hardware may be needed to provide environmental control. Cryostats, for example, cool superconducting qubits to keep them in a working state, and ion trap devices are used in trapped-ion qubit systems to confine ions using electromagnetic fields. <\/p>\n
Quantum control is critical to enable fault-tolerant quantum computingquantum computing in which as many errors as possible are prevented or suppressed. But realizing this capability on a large scale will require substantial innovation. Existing control systems are designed for a small number of qubits (1 to 1,000) and rely on customized calibration and dedicated resources for each qubit. A fault-tolerant quantum computer, on the other hand, needs to control 100,000 to 1,000,000 qubits simultaneously. Consequently, a transformative approach to quantum control design is essential. <\/p>\n
Specifically, to achieve fault-tolerant quantum computing on a large scale, there must be advances to address issues with current state-of-the-art quantum control system performance and scalability, as detailed below. <\/p>\n
Equipping quantum systems to perform at large scales will require the following: <\/p>\n
The limitations that physical space poses and the cost to power current quantum computing systems restrict the number of qubits that can be controlled with existing architecture, thus hindering large-scale computing. <\/p>\n
Challenges to overcoming these restrictions include the following: <\/p>\n
Several technologies show promise for scaling quantum control, although many are still in early-research or prototyping stages (Exhibit 3). <\/p>\n
Multiplexing could help reduce costs and prevent overheating. The cryogenic complementary metal-oxide-semiconductor (cryo-CMOS) approach also helps mitigate overheating; it is the most widely used approach across industries because it is currently the most straightforward way to add control lines, and it works well in a small-scale R&D setup. However, cryo-CMOS is close to reaching the maximum number of control lines, creating form factor and efficiency challenges to scaling. Even with improvements, the number of control lines would only be reduced by a few orders of magnitude, which is not sufficient for scaling to millions of qubits. Another option to address overheating is single-flux quantum technology, while optical links for microwave qubits can increase efficiency in interconnections as well as connect qubits between cryostats. <\/p>\n
Whether weighing options to supply quantum controls solutions or to invest in or integrate quantum technologies into companies in other sectors, leaders can better position their organizations for success by starting with a well-informed and strategically focused plan. <\/p>\n
The first strategic decision leaders in the quantum control sector must make is whether to buy or build their solutions. While various levels of quantum control solutions can be sourced from vendors, few companies specialize in control, and full-stack solutions for quantum computing are largely unavailable. The prevailing expertise is that vendors can offer considerable advantages in jump-starting quantum computing operations, especially those with complex and large-scale systems. Nevertheless, a lack of industrial standardization means that switching between quantum control vendors could result in additional costs down the road. Consequently, many leading quantum computing players opt to build their own quantum control. <\/p>\n
Ideally, business leaders also determine early on which parts of the quantum tech stack to focus their research capacities on and how to benchmark their technology. To develop capabilities and excel in quantum control, it is important to establish KPIs that are tailored to measure how effectively quantum control systems perform to achieve specific goals, such as improved qubit fidelity.5 This allows for the continuous optimization and refinement of quantum control techniques to improve overall system performance and scalability. <\/p>\n
Quantum control is key to creating business value. Thus, the maturity and scalability of control solutions are the chief considerations for leaders exploring business development related to quantum computing, quantum solutions integration, and quantum technologies investment. In addition to scalability (the key criterion for control solutions), leaders will need to consider and address the other control technology challenges noted previously. And as control technologies mature from innovations to large-scale solutions, establishing metrics for benchmarking them will be essential to assess, for example, ease of integration, cost effectiveness, error-suppression effectiveness, software offerings, and the possibility of standardizing across qubit technologies. <\/p>\n
Finally, given the shortage of quantum talent, recruiting and developing the highly specialized capabilities needed for each layer of the quantum stack is a top priority to ensure quantum control systems are properly developed and maintained. <\/p>\n
Henning Soller is a partner in McKinseys Frankfurt office, and Niko Mohr is a partner in the Dsseldorf office. Elisa Becker-Foss is a consultant in the New York office, Kamalika Dutta is a consultant in the Berlin office, Martina Gschwendtner is a consultant in the Munich office, Mena Issler is an associate partner in the Bay Area office, and Ming Xu is a consultant in the Stamford office. <\/p>\n
1 Entangled qubits are qubits that remain in a correlated state in which changes to one affect the other, even if they are separated by long distances. This property can enable massive performance boosts in information processing. 2 A quantum transpiler converts code from one quantum language to another while preserving and optimizing functionality to make algorithms and circuits portable between systems and devices. 3 Dynamical error suppression is one approach to suppressing quantum error and involves the periodic application of control pulse sequences to negate noise. 4 A qubit in a decoherent state is losing encoded quantum information (superposition and entanglement properties). 5 Qubit fidelity is a measure of the accuracy of a qubits state or the difference between its current state and the desired state. <\/p>\n
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Quantum control's role in scaling quantum computing - McKinsey<\/a><\/p>\n","protected":false},"excerpt":{"rendered":" June 14, 2024by Henning Soller and Niko Mohr with Elisa Becker-Foss, Kamalika Dutta, Martina Gschwendtner, Mena Issler, and Ming Xu Quantum computing can leverage the states of entangled qubits1 to solve problems that classical computing cannot currently solve and to substantially improve existing solutions. These qubits, which are typically constructed from photons, atoms, or ions, can only be manipulated using specially engineered signals with precisely controlled energy that is barely above that of a vacuum and that changes within nanoseconds Continue reading