Quantum Computing
Quantum Computing
What is quantum computing?
- Processing information with quantum mechanics
- Dramatically faster on certain hard problems
- Still early in commercialization
What quantum computing really changes is not computing speed but the shelf life of plans built on the assumption that something can't be solved. A company's security policies, long-term retention contracts, patent expiry dates, and materials-development roadmaps all rest on the assumption that 'this calculation will remain impossible.' Once that assumption wobbles, you have to redraw the schedule before you replace any equipment. That is why, at most companies, this topic reaches the agenda as a risk-management item, not a purchasing review.
Nor is there just one approach. Gate-based machines aimed at general-purpose computing and quantum annealing, which specializes in a single task, combinatorial optimization, serve entirely different purposes. The gap between physical and logical qubits is the key issue: the prevailing view in current research is that building one usable logical qubit through error correction takes hundreds to thousands of physical qubits. That is why the current stage is called noisy intermediate-scale quantum (NISQ) devices, which operate with errors. You will rarely buy hardware yourself; the usual route is access through the cloud, billed by usage time.
What reaches practice first is not computation but cryptography. 'Harvest now, decrypt later', where encrypted traffic is stored wholesale today and decrypted in the future, is treated as a threat model, and in 2024 the U.S. NIST formally published its post-quantum cryptography standards. The test is surprisingly simple: if your data's required retention period is 10 or 20 years, the transition discussion is already a present-tense matter; for logs you discard in three days, there is no reason to hurry. Data lifespan is your transition priority list.
The most common misconception is the sentence 'let's look into adopting quantum, too.' The core problem is that there is nothing to adopt yet. When this misconception leads to a PoC budget and a task force being set up first, a few months later all that remains is one demo screen and a spent budget. The opposite mistake is just as costly. While teams put it off as 'still far away,' ten-year contracts and medical and financial records are being stolen today and stored somewhere. The right order is to build an inventory of cryptographic assets first; hardware and algorithm questions come after.
Ara LimExpert in global leadership, AI, and business communication
Seungtae ParkAX practitioner who teaches AI business development and marketing automation by building it hands-onRichard Feynman and others proposed the concept in the 1980s, and it has grown into research on putting quantum mechanics to work in computation.
Let's look at a hypothetical case. Company A is a Korean payment gateway (PG) provider with 210 employees and KRW 3 trillion (≈ US$2,244,501,000) in annual transaction volume. When a 'post-quantum cryptography transition plan' item was added to an overseas card brand's annual security due-diligence questionnaire, the security division was given 90 days to respond. No one in the company could explain quantum computing.
- In the first meeting, the CISO drew a line: 'This isn't about buying a quantum computer. Let's first count when our cryptography goes bad.' The CISO launched a six-week cryptographic asset survey first.
- The infrastructure team lead scanned 1,400 servers and found 2,317 RSA-2048 certificates and 41TB of merchant settlement data with a 10-year retention requirement, and this 41TB was confirmed as the first priority for migration.
- The head of data analytics reviewed a proposal to use quantum optimization for the settlement batch and reported, 'a commercial solver finishes it in 8 minutes today.' The KRW 300 million (≈ US$224,500) PoC plan was shelved on the spot.
- The security architect opened a learning account on a cloud quantum service for just KRW 400,000 (≈ US$300) a month. After 12 developers ran it hands-on, the team agreed: 'this isn't our problem right now.'
- Starting with external integration points, they switched to a hybrid approach that runs legacy and post-quantum algorithms together, and confirmed that added response latency stayed within 3ms across 12 pilot channels.
- Ten months later, the questionnaire item passed as 'roadmap in place,' the migration rate for external channels rose from 0% to 78%, and the remaining 22 channels were put on a quarterly migration schedule.